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@ -92,14 +92,13 @@ SUMMARY_PROMPT = """ |
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如果检索到的信息不足以回答问题，你应该使用你的知识来进行扩展补充。 |
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注意，不要逐个回答问题，而是应该综合所有问题和信息，生成一个完整的回答。 |
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同时，你应该根据提供的信息生成文内引用"[^index]"(markdown文内引用)。 |
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如果你自己提供的信息需要使用"[^0]"引用，即你提供的信息使用固定index=0。 |
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来自<chunk><reference>的引用序号从[^index]从index=1开始，来源需要与前文<reference>中的"href"一致 |
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不需要对每个<chunk>分配一个引用，而是相同<reference>的<chunk>共用一个引用。 |
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来自<chunk><reference>的引用序号从[^index]从index=1开始，来源需要与前文<reference>中的"id"一致。 |
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不需要对每个<chunk>分配一个引用，而是相同<reference>的<chunk>共用引用，并确保每一个<reference>都被引用。 |
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另外，如果回答的内容文内引用需要引用多个<reference>，请添加多个[^index]到句尾。 |
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<EXAMPLE> |
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"XGBoost是非常强大的集成学习模型。[^1]但是XGBoost的缺点是计算复杂度高，需要大量的计算资源。[^0]" |
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"XGBoost是非常强大的集成学习模型。[^1]但是XGBoost的缺点是计算复杂度高，需要大量的计算资源。[^2]" |
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</EXAMPLE> |
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@ -152,10 +151,6 @@ class DeepSearch(BaseAgent): |
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self.vector_db = vector_db |
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self.max_iter = max_iter |
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self.route_collection = route_collection |
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self.all_collections = [ |
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collection_info.collection_name |
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for collection_info in self.vector_db.list_collections(dim=embedding_model.dimension) |
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] |
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self.text_window_splitter = text_window_splitter |
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def invoke(self, query: str, dim: int, **kwargs) -> list[str]: |
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@ -227,7 +222,10 @@ class DeepSearch(BaseAgent): |
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query=query, dim=self.embedding_model.dimension |
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) |
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else: |
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selected_collections = self.all_collections |
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selected_collections = [ |
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collection_info.collection_name |
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for collection_info in self.vector_db.list_collections(dim=self.embedding_model.dimension) |
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] |
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all_retrieved_results = [] |
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query_vector = self.embedding_model.embed_query(query) |
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@ -394,7 +392,7 @@ class DeepSearch(BaseAgent): |
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if not all_retrieved_results or len(all_retrieved_results) == 0: |
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send_info(f"'{original_query}'没能找到更多信息！") |
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return "", [] |
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chunks, refs = self._format_chunks(all_retrieved_results) |
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chunks, refs = self._format_chunks(all_retrieved_results, with_chunk_id=False) |
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send_info(f"正在总结 {len(all_retrieved_results)} 个查找到的文档片段") |
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summary_prompt = SUMMARY_PROMPT.format( |
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original_query=original_query, |
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@ -406,13 +404,13 @@ class DeepSearch(BaseAgent): |
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send_answer(response) |
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return response, all_retrieved_results |
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def _format_chunks(self, retrieved_results: list[RetrievalResult]) -> tuple[str, str]: |
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def _format_chunks(self, retrieved_results: list[RetrievalResult], with_chunk_id: bool = True) -> tuple[str, str]: |
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# 以referecen为key，把chunk放到字典中 |
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ref_dict = defaultdict(list) |
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for result in retrieved_results: |
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ref_dict[result.reference].append(result.text) |
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formated_chunks = [] |
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formated_refs = ["\n\n[^0]: AI 生成\n"] |
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formated_refs = ["\n\n"] |
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chunk_count = 0 |
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for i, reference in enumerate(ref_dict): |
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formated_chunk = "".join( |
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@ -421,6 +419,11 @@ class DeepSearch(BaseAgent): |
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f"<reference id='{i + 1}' href='{reference}'>" + |
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f"<chunk id='{j + 1 + chunk_count}'>\n{chunk}\n</chunk id='{j + 1 + chunk_count}'>" + |
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f"</reference id='{i + 1}'>\n" |
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) |
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if with_chunk_id else ( |
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f"<reference id='{i + 1}' href='{reference}'>" + |
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f"<chunk>\n{chunk}\n</chunk>" + |
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f"</reference id='{i + 1}'>\n" |
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) |
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for j, chunk in enumerate(ref_dict[reference]) |
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] |
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@ -428,7 +431,7 @@ class DeepSearch(BaseAgent): |
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print(formated_chunk) |
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formated_chunks.append(formated_chunk) |
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chunk_count += len(ref_dict[reference]) |
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formated_refs.append(f"[^{i + 1}]: " + str(reference) + "\n") |
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formated_refs.append(f"[{i + 1}]: " + str(reference) + "\n") |
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formated_chunks = "".join(formated_chunks) |
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formated_refs = "".join(formated_refs) |
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return formated_chunks, formated_refs |
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