可视化FAISS矢量空间并调整RAG参数提高结果精度

 pip install langchain faiss-cpu sentence-transformers flask-sqlalchemy psutil unstructured pdf2image unstructured_inference pillow_heif opencv-python pikepdf pypdf
 pip install renumics-spotlight
 CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install --upgrade --force-reinstall llama-cpp-python --no-cache-dir

 # LoadFVectorize.py
 
 from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_community.document_loaders import OnlinePDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import FAISS
 
 # access an online pdf
 def load_doc() -> 'List[Document]':
     loader = OnlinePDFLoader("https://support.riverbed.com/bin/support/download?did=7q6behe7hotvnpqd9a03h1dji&version=9.15.0")
     documents = loader.load()
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=100)
     docs = text_splitter.split_documents(documents)
     return docs
 
 # vectorize and commit to disk
 def vectorize(embeddings_model) -> 'FAISS':
     docs = load_doc()
     db = FAISS.from_documents(docs, embeddings_model)
     db.save_local("./opdf_index")
    return db
 
 # attempts to load vectorstore from disk
 def load_db() -> 'FAISS':
     embeddings_model = HuggingFaceEmbeddings()
     try:
         db = FAISS.load_local("./opdf_index", embeddings_model)
    except Exception as e:
         print(f'Exception: {e}\nNo index on disk, creating new...')
         db = vectorize(embeddings_model)
     return db

 # main.py
 from langchain.chains import RetrievalQA
 from langchain.prompts import PromptTemplate
 from langchain_community.llms import LlamaCpp
 from langchain_community.embeddings import HuggingFaceEmbeddings
 import LoadFVectorize
 from renumics import spotlight
 import pandas as pd
 import numpy as np
 
 # Prompt template 
 qa_template = """<|system|>
 You are a friendly chatbot who always responds in a precise manner. If answer is 
 unknown to you, you will politely say so.
 Use the following context to answer the question below:
 {context}</s>
 <|user|>
 {question}</s>
 <|assistant|>
 """
 
 # Create a prompt instance 
 QA_PROMPT = PromptTemplate.from_template(qa_template)
 # load LLM
 llm = LlamaCpp(
     model_path="./models/tinyllama_gguf/tinyllama-1.1b-chat-v1.0.Q5_K_M.gguf",
     temperature=0.01,
     max_tokens=2000,
     top_p=1,
     verbose=False,
     n_ctx=2048
 )
 # vectorize and create a retriever
 db = LoadFVectorize.load_db()
 faiss_retriever = db.as_retriever(search_type="mmr", search_kwargs={'fetch_k': 3}, max_tokens_limit=1000)
 # Define a QA chain 
 qa_chain = RetrievalQA.from_chain_type(
     llm,
     retriever=faiss_retriever,
     chain_type_kwargs={"prompt": QA_PROMPT}
 )
 
 query = 'What versions of TLS supported by Client Accelerator 6.3.0?'
 
 result = qa_chain({"query": query})
 print(f'--------------\nQ: {query}\nA: {result["result"]}')
 
 visualize_distance(db,query,result["result"])

     vs = db.__dict__.get("docstore")
     index_list = db.__dict__.get("index_to_docstore_id").values()
     doc_cnt = db.index.ntotal

    embeddings_vec = db.index.reconstruct_n()

    doc_list = list() 
    for i,doc-id in enumerate(index_list):
        a_doc = vs.search(doc-id)
        doc_list.append([doc-id,a_doc.metadata.get("source"),a_doc.page_content,embeddings_vec[i]])

     df = pd.DataFrame(doc_list,columns=['id','metadata','document','embedding'])

     # add rows for question and answer
     embeddings_model = HuggingFaceEmbeddings()
     question_df = pd.DataFrame(
         {
             "id": "question",
             "question": question,
             "embedding": [embeddings_model.embed_query(question)],
         })
     answer_df = pd.DataFrame(
         {
             "id": "answer",
             "answer": answer,
             "embedding": [embeddings_model.embed_query(answer)],
         })
     df = pd.concat([question_df, answer_df, df])

     question_embedding = embeddings_model.embed_query(question)
     # add column for vector distance
     df["dist"] = df.apply(                                                                                                                                                                         
         lambda row: np.linalg.norm(
             np.array(row["embedding"]) - question_embedding
         ),axis=1,)

 spotlight.show(df)

 def load_doc():
     loader = WebBaseLoader(['https://en.wikipedia.org/wiki/66th_Annual_Grammy_Awards','https://en.wikipedia.org/wiki/James_Webb_Space_Telescope'])
     documents = loader.load()
     ...