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Refactor logic into separate files for easier reading
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,147 @@ | ||
| from langchain.embeddings.openai import OpenAIEmbeddings | ||
| from langchain.embeddings import OllamaEmbeddings, SentenceTransformerEmbeddings | ||
| from langchain.chat_models import ChatOpenAI, ChatOllama | ||
| from langchain.vectorstores.neo4j_vector import Neo4jVector | ||
| from langchain.chains import RetrievalQAWithSourcesChain | ||
| from langchain.chains.qa_with_sources import load_qa_with_sources_chain | ||
| from langchain.prompts.chat import ( | ||
| ChatPromptTemplate, | ||
| SystemMessagePromptTemplate, | ||
| HumanMessagePromptTemplate, | ||
| ) | ||
| from typing import List, Any | ||
| from utils import BaseLogger | ||
|
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| def load_embedding_model(embedding_model_name: str, logger=BaseLogger(), config={}): | ||
| if embedding_model_name == "ollama": | ||
| embeddings = OllamaEmbeddings(base_url=config.ollama_base_url, model="llama2") | ||
| dimension = 4096 | ||
| logger.info("Embedding: Using Ollama") | ||
| elif embedding_model_name == "openai": | ||
| embeddings = OpenAIEmbeddings() | ||
| dimension = 1536 | ||
| logger.info("Embedding: Using OpenAI") | ||
| else: | ||
| embeddings = SentenceTransformerEmbeddings( | ||
| model_name="all-MiniLM-L6-v2", cache_folder="/embedding_model" | ||
| ) | ||
| dimension = 384 | ||
| logger.info("Embedding: Using SentenceTransformer") | ||
| return embeddings, dimension | ||
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| def load_llm(llm_name: str, logger=BaseLogger(), config={}): | ||
| if llm_name == "gpt-4": | ||
| logger.info("LLM: Using GPT-4") | ||
| return ChatOpenAI(temperature=0, model_name="gpt-4", streaming=True) | ||
| elif llm_name == "gpt-3.5": | ||
| logger.info("LLM: Using GPT-3.5") | ||
| return ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo", streaming=True) | ||
| elif len(llm_name): | ||
| logger.info(f"LLM: Using Ollama: {llm_name}") | ||
| return ChatOllama( | ||
| temperature=0, | ||
| base_url=config["ollama_base_url"], | ||
| model=llm_name, | ||
| streaming=True, | ||
| top_k=10, # A higher value (100) will give more diverse answers, while a lower value (10) will be more conservative. | ||
| top_p=0.3, # Higher value (0.95) will lead to more diverse text, while a lower value (0.5) will generate more focused text. | ||
| num_ctx=3072, # Sets the size of the context window used to generate the next token. | ||
| ) | ||
| logger.info("LLM: Using GPT-3.5") | ||
| return ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo", streaming=True) | ||
|
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| def configure_llm_only_chain(llm): | ||
| # LLM only response | ||
| template = """ | ||
| You are a helpful assistant that helps a support agent with answering programming questions. | ||
| If you don't know the answer, just say that you don't know, don't try to make up an answer. | ||
| """ | ||
| system_message_prompt = SystemMessagePromptTemplate.from_template(template) | ||
| human_template = "{text}" | ||
| human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) | ||
| chat_prompt = ChatPromptTemplate.from_messages( | ||
| [system_message_prompt, human_message_prompt] | ||
| ) | ||
|
|
||
| def generate_llm_output( | ||
| user_input: str, callbacks: List[Any], prompt=chat_prompt | ||
| ) -> str: | ||
| answer = llm( | ||
| prompt.format_prompt( | ||
| text=user_input, | ||
| ).to_messages(), | ||
| callbacks=callbacks, | ||
| ).content | ||
| return {"answer": answer} | ||
|
|
||
| return generate_llm_output | ||
|
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| def configure_qa_rag_chain(llm, embeddings, embeddings_store_url, username, password): | ||
| # RAG response | ||
| general_system_template = """ | ||
| Use the following pieces of context to answer the question at the end. | ||
| The context contains question-answer pairs and their links from Stackoverflow. | ||
| You should prefer information from accepted or more upvoted answers. | ||
| Make sure to rely on information from the answers and not on questions to provide accuate responses. | ||
| When you find particular answer in the context useful, make sure to cite it in the answer using the link. | ||
| If you don't know the answer, just say that you don't know, don't try to make up an answer. | ||
| ---- | ||
| {summaries} | ||
| ---- | ||
| Each answer you generate should contain a section at the end of links to | ||
| Stackoverflow questions and answers you found useful, which are described under Source value. | ||
| You can only use links to StackOverflow questions that are present in the context and always | ||
| add links to the end of the answer in the style of citations. | ||
| Generate concise answers with references sources section of links to | ||
| relevant StackOverflow questions only at the end of the answer. | ||
| """ | ||
| general_user_template = "Question:```{question}```" | ||
| messages = [ | ||
| SystemMessagePromptTemplate.from_template(general_system_template), | ||
| HumanMessagePromptTemplate.from_template(general_user_template), | ||
| ] | ||
| qa_prompt = ChatPromptTemplate.from_messages(messages) | ||
|
|
||
| qa_chain = load_qa_with_sources_chain( | ||
| llm, | ||
| chain_type="stuff", | ||
| prompt=qa_prompt, | ||
| ) | ||
|
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||
| # Vector + Knowledge Graph response | ||
| kg = Neo4jVector.from_existing_index( | ||
| embedding=embeddings, | ||
| url=embeddings_store_url, | ||
| username=username, | ||
| password=password, | ||
| database="neo4j", # neo4j by default | ||
| index_name="stackoverflow", # vector by default | ||
| text_node_property="body", # text by default | ||
| retrieval_query=""" | ||
| WITH node AS question, score AS similarity | ||
| CALL { with question | ||
| MATCH (question)<-[:ANSWERS]-(answer) | ||
| WITH answer | ||
| ORDER BY answer.is_accepted DESC, answer.score DESC | ||
| WITH collect(answer)[..2] as answers | ||
| RETURN reduce(str='', answer IN answers | str + | ||
| '\n### Answer (Accepted: '+ answer.is_accepted + | ||
| ' Score: ' + answer.score+ '): '+ answer.body + '\n') as answerTexts | ||
| } | ||
| RETURN '##Question: ' + question.title + '\n' + question.body + '\n' | ||
| + answerTexts AS text, similarity as score, {source: question.link} AS metadata | ||
| ORDER BY similarity ASC // so that best answers are the last | ||
| """, | ||
| ) | ||
|
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||
| kg_qa = RetrievalQAWithSourcesChain( | ||
| combine_documents_chain=qa_chain, | ||
| retriever=kg.as_retriever(search_kwargs={"k": 2}), | ||
| reduce_k_below_max_tokens=False, | ||
| max_tokens_limit=3375, | ||
| ) | ||
| return kg_qa |
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