langchain-ChatGLM源码阅读：参数设置

上下文关联

上下文关联相关参数：

• 知识相关度阈值score_threshold
• 内容条数k
• 是否启用上下文关联chunk_conent
• 上下文最大长度chunk_size

其主要作用是在所在文档中扩展与当前query相似度较高的知识库的内容，作为相关信息与query按照prompt规则组合后作为输入获得模型的回答。

• 获取查询句query嵌入：faiss.py

def similarity_search_with_score(
        self, query: str, k: int = 4
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to query.

        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.

        Returns:
            List of Documents most similar to the query and score for each
        """
        embedding = self.embedding_function(query)
        docs = self.similarity_search_with_score_by_vector(embedding, k)
        return docs

• 上下文生成：MyFAISS.py

    def seperate_list(self, ls: List[int]) -> List[List[int]]:
        # TODO: 增加是否属于同一文档的判断
        lists = []
        ls1 = [ls[0]]
        for i in range(1, len(ls)):
            if ls[i - 1] + 1 == ls[i]:
                ls1.append(ls[i])
            else:
                lists.append(ls1)
                ls1 = [ls[i]]
        lists.append(ls1)
        return lists

    def similarity_search_with_score_by_vector(
            self, embedding: List[float], k: int = 4
    ) -> List[Document]:
        faiss = dependable_faiss_import()
        # (1,1024)
        vector = np.array([embedding], dtype=np.float32)
        # 默认FALSE
        if self._normalize_L2:
            faiss.normalize_L2(vector)
        # shape均为(1, k)，这步获取与query有top-k相似度的知识库
        scores, indices = self.index.search(vector, k)
        docs = []
        id_set = set()
        store_len = len(self.index_to_docstore_id)
        rearrange_id_list = False
        # 遍历找到的k个最相似知识库的索引
        # k是第一次的筛选条件，score是第二次的筛选条件
        for j, i in enumerate(indices[0]):
            if i == -1 or 0 < self.score_threshold < scores[0][j]:
                # This happens when not enough docs are returned.
                continue
            if i in self.index_to_docstore_id:
                _id = self.index_to_docstore_id[i]
            # 执行接下来的操作
            else:
                continue
            # index→id→content
            doc = self.docstore.search(_id)
            if (not self.chunk_conent) or ("context_expand" in doc.metadata and not doc.metadata["context_expand"]):
                # 匹配出的文本如果不需要扩展上下文则执行如下代码
                if not isinstance(doc, Document):
                    raise ValueError(f"Could not find document for id {_id}, got {doc}")
                doc.metadata["score"] = int(scores[0][j])
                docs.append(doc)
                continue
            # 其实存的都是index
            id_set.add(i)
            docs_len = len(doc.page_content)
            # 跟外部变量定义的k重名了，烂
            # 一个知识库是分句后得到的一句话，i是当前知识库在总知识库中的位置，store_len是总知识库大小
            # 所以k说的是扩充上下文时最多能跨多少个知识库
            for k in range(1, max(i, store_len - i)):
                break_flag = False
                if "context_expand_method" in doc.metadata and doc.metadata["context_expand_method"] == "forward":
                    expand_range = [i + k]
                elif "context_expand_method" in doc.metadata and doc.metadata["context_expand_method"] == "backward":
                    expand_range = [i - k]
                else:
                    # i=4922, k=1 → [4923, 4921]
                    expand_range = [i + k, i - k]
                for l in expand_range:
                    # 确保扩展上下文时不会造成重复
                    if l not in id_set and 0 <= l < len(self.index_to_docstore_id):
                        _id0 = self.index_to_docstore_id[l]
                        doc0 = self.docstore.search(_id0)
                        # 如果当前字数大于250或者是知识库跨了文件，扩充上下文过程终止
                        # 这一句有些问题，有一端跨文件就终止，应该是两端同时跨才终止才对
                        if docs_len + len(doc0.page_content) > self.chunk_size or doc0.metadata["source"] != \
                                doc.metadata["source"]:
                            break_flag = True
                            break
                        elif doc0.metadata["source"] == doc.metadata["source"]:
                            docs_len += len(doc0.page_content)
                            id_set.add(l)
                            rearrange_id_list = True
                if break_flag:
                    break
        # 如果没有扩展上下文（不需要或是不能）
        if (not self.chunk_conent) or (not rearrange_id_list):
            return docs
        if len(id_set) == 0 and self.score_threshold > 0:
            return []
        id_list = sorted(list(id_set))
        # 连续必然属于同一文档，但不连续也可能在同一文档
        # 返回二级列表，第一级是连续的index列表，第二级是具体index
        id_lists = self.seperate_list(id_list)
        for id_seq in id_lists:
            for id in id_seq:
                if id == id_seq[0]:
                    _id = self.index_to_docstore_id[id]
                    # doc = self.docstore.search(_id)
                    doc = copy.deepcopy(self.docstore.search(_id))
                else:
                    _id0 = self.index_to_docstore_id[id]
                    doc0 = self.docstore.search(_id0)
                    doc.page_content += " " + doc0.page_content
            if not isinstance(doc, Document):
                raise ValueError(f"Could not find document for id {_id}, got {doc}")
            # indices为相关文件的索引
            # 因为可能会将多个连续的id合并，因此需要将同一seq内所有位于top-k的知识库的分数取最小值作为seq对应的分数
            doc_score = min([scores[0][id] for id in [indices[0].tolist().index(i) for i in id_seq if i in indices[0]]])
            doc.metadata["score"] = int(doc_score)
            docs.append(doc)
        # 注意这里docs没有按相似度排序，可以自行加个sort
        return docs

考虑到相似度计算在双向扩展上有一些问题，对算法做了一些修改：

 def similarity_search_with_score_by_vector(
            self, embedding: List[float], k: int = 4
    ) -> List[Document]:
        faiss = dependable_faiss_import()
        # (1,1024)
        vector = np.array([embedding], dtype=np.float32)
        # 默认FALSE
        if self._normalize_L2:
            faiss.normalize_L2(vector)
        # shape均为(1, k)
        scores, indices = self.index.search(vector, k)
        docs = []
        id_set = set()
        store_len = len(self.index_to_docstore_id)
        rearrange_id_list = False
        # 存储关键句
        keysentences = []
        # 遍历找到的k个最近相关文档的索引
        # top-k是第一次的筛选条件，score是第二次的筛选条件
        for j, i in enumerate(indices[0]):
            if i == -1 or 0 < self.score_threshold < scores[0][j]:
                # This happens when not enough docs are returned.
                continue
            if i in self.index_to_docstore_id:
                _id = self.index_to_docstore_id[i]
            # 执行接下来的操作
            else:
                continue
            # index→id→content
            doc = self.docstore.search(_id)
            if (not self.chunk_conent) or ("context_expand" in doc.metadata and not doc.metadata["context_expand"]):
                # 匹配出的文本如果不需要扩展上下文则执行如下代码
                if not isinstance(doc, Document):
                    raise ValueError(f"Could not find document for id {_id}, got {doc}")
                doc.metadata["score"] = int(scores[0][j])
                docs.append(doc)
                continue
            # 其实存的都是index
            id_set.add(i)
            docs_len = len(doc.page_content.strip())
            # 跟外部变量定义的k重名了，烂
            # 一个知识库是分句后得到的一句话，i是当前知识库在总知识库中的位置，store_len是总知识库大小
            # 所以k说的是扩充上下文时最多能跨多少个知识库
            for k in range(1, max(i, store_len - i)):
                single_break_flag = 0
                double_break_flag = 0
                if "context_expand_method" in doc.metadata and doc.metadata["context_expand_method"] == "forward":
                    expand_range = [i + k]
                elif "context_expand_method" in doc.metadata and doc.metadata["context_expand_method"] == "backward":
                    expand_range = [i - k]
                else:
                    # i=4922, k=1 → [4923, 4921]
                    expand_range = [i + k, i - k]
                for l in expand_range:
                    # 确保扩展上下文时不会造成重复
                    if l not in id_set and 0 <= l < len(self.index_to_docstore_id):
                        _id0 = self.index_to_docstore_id[l]
                        doc0 = self.docstore.search(_id0)
                        # 如果当前字数大于250或者是知识库跨了文件，扩充上下文过程终止
                        # 这一句有些问题，有一端跨文件就终止，应该是两端同时跨才终止才对
                        if docs_len + len(doc0.page_content.strip()) > self.chunk_size or doc0.metadata["source"] != \
                                doc.metadata["source"]:
                            single_break_flag = 1
                            if docs_len + len(doc0.page_content.strip()) > self.chunk_size:
                                double_break_flag = 2
                                break
                            else:
                                double_break_flag += 1
                        elif doc0.metadata["source"] == doc.metadata["source"]:
                            docs_len += len(doc0.page_content.strip())
                            id_set.add(l)
                            rearrange_id_list = True
                if double_break_flag == 2:
                    break
        # 如果没有扩展上下文（不需要或是不能）
        if (not self.chunk_conent) or (not rearrange_id_list):
            return docs, keysentences
        if len(id_set) == 0 and self.score_threshold > 0:
            return [], []
        id_list = sorted(list(id_set))
        # 连续必然属于同一文档，但不连续也可能在同一文档
        # 返回二级列表，第一级是连续的index列表，第二级是具体index
        id_lists = self.seperate_list(id_list)
        keyids = indices[0].tolist()
        filter_rate = 0.05
        for id_seq in id_lists:
            seqsentences = []
            for id_index, id in enumerate(id_seq):
                if id == id_seq[0]:
                    _id = self.index_to_docstore_id[id]
                    # doc = self.docstore.search(_id)
                    doc = copy.deepcopy(self.docstore.search(_id))
                    if id in keyids:
                        seqsentences.append({'key': doc.page_content.strip(), 'rate': id_index/len(id_seq)})
                        doc.page_content = "<b>"+doc.page_content.strip()+"</b>"
                else:
                    _id0 = self.index_to_docstore_id[id]
                    doc0 = self.docstore.search(_id0)
                    if id in keyids:
                        seqsentences.append({'key': doc0.page_content.strip(), 'rate': id_index/len(id_seq)})
                        doc.page_content += " <b>" + doc0.page_content.strip()+"</b>"
                    else:
                        doc.page_content += " " + doc0.page_content.strip()
            if not isinstance(doc, Document):
                raise ValueError(f"Could not find document for id {_id}, got {doc}")
            # indices为相关文件的索引
            # 因为可能会将多个连续的id合并，因此需要将同一seq内所有位于top-k的知识库的分数取最小值作为seq对应的分数
            doc_score = min([scores[0][id] for id in [indices[0].tolist().index(i) for i in id_seq if i in indices[0]]])
            doc.metadata["score"] = int(doc_score)
            docs.append(doc)
            seqsentences.sort(key=lambda data: data['rate'])
            if seqsentences[-1]['rate'] < filter_rate:
                keysentences.append(seqsentences[-1]['key'])
            else:
                keysentences += [seqsentence['key'] for seqsentence in seqsentences if seqsentence['rate'] > filter_rate]
        docs.sort(key=lambda doc: doc.metadata['score'])
        return docs, keysentences

• prompt生成：local_doc_qa.py

def get_knowledge_based_answer(self, query, vs_path, chat_history=[], streaming: bool = STREAMING):
        related_docs_with_score = vector_store.similarity_search_with_score(query, k=self.top_k)
        torch_gc()
        if len(related_docs_with_score) > 0:
            prompt = generate_prompt(related_docs_with_score, query)
        else:
            prompt = query
        answer_result_stream_result = self.llm_model_chain(
            {"prompt": prompt, "history": chat_history, "streaming": streaming})

def generate_prompt(related_docs: List[str],
                    query: str,
                    prompt_template: str = PROMPT_TEMPLATE, ) -> str:
    context = "\n".join([doc.page_content for doc in related_docs])
    prompt = prompt_template.replace("{question}", query).replace("{context}", context)
    return prompt

对话轮数

其实就是要存多少历史记录，如果为0的话就是在执行当前对话时不考虑历史问答

• 模型内部调用时使用，以chatglm为例：chatglm_llm.py

 def _generate_answer(self,
                         inputs: Dict[str, Any],
                         run_manager: Optional[CallbackManagerForChainRun] = None,
                         generate_with_callback: AnswerResultStream = None) -> None:
        history = inputs[self.history_key]
        streaming = inputs[self.streaming_key]
        prompt = inputs[self.prompt_key]
        print(f"__call:{prompt}")
        # Create the StoppingCriteriaList with the stopping strings
        stopping_criteria_list = transformers.StoppingCriteriaList()
        # 定义模型stopping_criteria 队列，在每次响应时将 torch.LongTensor, torch.FloatTensor同步到AnswerResult
        listenerQueue = AnswerResultQueueSentinelTokenListenerQueue()
        stopping_criteria_list.append(listenerQueue)
        if streaming:
            history += [[]]
            for inum, (stream_resp, _) in enumerate(self.checkPoint.model.stream_chat(
                    self.checkPoint.tokenizer,
                    prompt,
                    # 为0时history返回[]
                    history=history[-self.history_len:-1] if self.history_len > 0 else [],
                    max_length=self.max_token,
                    temperature=self.temperature,
                    top_p=self.top_p,
                    top_k=self.top_k,
                    stopping_criteria=stopping_criteria_list
            )):

向量匹配 top k

虽然放在了模型配置那一页，但实际上还是用来控制上下文关联里面的内容条数k的，不知道为什么写了两遍。。。

控制生成质量的参数

这些参数没有在前端显式地给出，而是写死在了模型定义里文章来源地址https://www.toymoban.com/news/detail-630544.html

• 模型定义，以chatglm为例：chatglm_llm.py

class ChatGLMLLMChain(BaseAnswer, Chain, ABC):
    max_token: int = 10000
    temperature: float = 0.01
    # 相关度
    top_p = 0.4
    # 候选词数量
    top_k = 10
    checkPoint: LoaderCheckPoint = None
    # history = []
    history_len: int = 10

参数设置心得

• score_threshold和k设太小会找不到问题对应的原文件，太大找到一堆不相关的
• chunk_size设太小不能在原文件里找到问题对应的原文，太大无法有效归纳出答案
• temperature和top_p默认值下生成的答案基本固定，但也很死板；过大的temperature导致回答的事实不稳定；过大的top_p导致回答的语言风格不稳定；调整top_k没发现结果有什么变化

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