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Refactor Chunk API (#2855)
### What problem does this PR solve? Refactor Chunk API #2846 ### Type of change - [x] Refactoring --------- Co-authored-by: liuhua <10215101452@stu.ecun.edu.cn> Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
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liuhua
GitHub
parent
b9fa00f341
commit
dab92ac1e8
@ -119,13 +119,11 @@ def update_doc(tenant_id, dataset_id, document_id):
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if informs:
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e, file = FileService.get_by_id(informs[0].file_id)
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FileService.update_by_id(file.id, {"name": req["name"]})
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if "parser_config" in req:
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DocumentService.update_parser_config(doc.id, req["parser_config"])
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if "parser_method" in req:
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if doc.parser_id.lower() == req["parser_method"].lower():
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if "parser_config" in req:
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if req["parser_config"] == doc.parser_config:
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return get_result(retcode=RetCode.SUCCESS)
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else:
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return get_result(retcode=RetCode.SUCCESS)
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return get_result()
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if doc.type == FileType.VISUAL or re.search(
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r"\.(ppt|pptx|pages)$", doc.name):
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@ -146,8 +144,6 @@ def update_doc(tenant_id, dataset_id, document_id):
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return get_error_data_result(retmsg="Tenant not found!")
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ELASTICSEARCH.deleteByQuery(
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Q("match", doc_id=doc.id), idxnm=search.index_name(tenant_id))
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if "parser_config" in req:
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DocumentService.update_parser_config(doc.id, req["parser_config"])
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return get_result()
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@ -258,6 +254,8 @@ def parse(tenant_id,dataset_id):
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if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
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return get_error_data_result(retmsg=f"You don't own the dataset {dataset_id}.")
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req = request.json
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if not req.get("document_ids"):
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return get_error_data_result("`document_ids` is required")
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for id in req["document_ids"]:
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if not DocumentService.query(id=id,kb_id=dataset_id):
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return get_error_data_result(retmsg=f"You don't own the document {id}.")
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@ -283,9 +281,14 @@ def stop_parsing(tenant_id,dataset_id):
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if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
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return get_error_data_result(retmsg=f"You don't own the dataset {dataset_id}.")
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req = request.json
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if not req.get("document_ids"):
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return get_error_data_result("`document_ids` is required")
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for id in req["document_ids"]:
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if not DocumentService.query(id=id,kb_id=dataset_id):
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doc = DocumentService.query(id=id, kb_id=dataset_id)
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if not doc:
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return get_error_data_result(retmsg=f"You don't own the document {id}.")
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if doc[0].progress == 100.0 or doc[0].progress == 0.0:
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return get_error_data_result("Can't stop parsing document with progress at 0 or 100")
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info = {"run": "2", "progress": 0}
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DocumentService.update_by_id(id, info)
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# if str(req["run"]) == TaskStatus.CANCEL.value:
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@ -297,7 +300,7 @@ def stop_parsing(tenant_id,dataset_id):
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@manager.route('/dataset/<dataset_id>/document/<document_id>/chunk', methods=['GET'])
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@token_required
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def list_chunk(tenant_id,dataset_id,document_id):
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def list_chunks(tenant_id,dataset_id,document_id):
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if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
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return get_error_data_result(retmsg=f"You don't own the dataset {dataset_id}.")
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doc=DocumentService.query(id=document_id, kb_id=dataset_id)
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@ -309,57 +312,58 @@ def list_chunk(tenant_id,dataset_id,document_id):
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page = int(req.get("offset", 1))
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size = int(req.get("limit", 30))
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question = req.get("keywords", "")
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try:
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query = {
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"doc_ids": [doc_id], "page": page, "size": size, "question": question, "sort": True
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query = {
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"doc_ids": [doc_id], "page": page, "size": size, "question": question, "sort": True
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}
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sres = retrievaler.search(query, search.index_name(tenant_id), highlight=True)
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res = {"total": sres.total, "chunks": [], "doc": doc.to_dict()}
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origin_chunks = []
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sign = 0
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for id in sres.ids:
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d = {
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"chunk_id": id,
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"content_with_weight": rmSpace(sres.highlight[id]) if question and id in sres.highlight else sres.field[
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id].get(
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"content_with_weight", ""),
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"doc_id": sres.field[id]["doc_id"],
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"docnm_kwd": sres.field[id]["docnm_kwd"],
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"important_kwd": sres.field[id].get("important_kwd", []),
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"img_id": sres.field[id].get("img_id", ""),
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"available_int": sres.field[id].get("available_int", 1),
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"positions": sres.field[id].get("position_int", "").split("\t")
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}
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if "available_int" in req:
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query["available_int"] = int(req["available_int"])
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sres = retrievaler.search(query, search.index_name(tenant_id), highlight=True)
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res = {"total": sres.total, "chunks": [], "doc": doc.to_dict()}
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if len(d["positions"]) % 5 == 0:
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poss = []
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for i in range(0, len(d["positions"]), 5):
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poss.append([float(d["positions"][i]), float(d["positions"][i + 1]), float(d["positions"][i + 2]),
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float(d["positions"][i + 3]), float(d["positions"][i + 4])])
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d["positions"] = poss
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origin_chunks = []
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for id in sres.ids:
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d = {
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"chunk_id": id,
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"content_with_weight": rmSpace(sres.highlight[id]) if question and id in sres.highlight else sres.field[
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id].get(
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"content_with_weight", ""),
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"doc_id": sres.field[id]["doc_id"],
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"docnm_kwd": sres.field[id]["docnm_kwd"],
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"important_kwd": sres.field[id].get("important_kwd", []),
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"img_id": sres.field[id].get("img_id", ""),
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"available_int": sres.field[id].get("available_int", 1),
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"positions": sres.field[id].get("position_int", "").split("\t")
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}
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if len(d["positions"]) % 5 == 0:
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poss = []
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for i in range(0, len(d["positions"]), 5):
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poss.append([float(d["positions"][i]), float(d["positions"][i + 1]), float(d["positions"][i + 2]),
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float(d["positions"][i + 3]), float(d["positions"][i + 4])])
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d["positions"] = poss
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origin_chunks.append(d)
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if req.get("id"):
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if req.get("id") == id:
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origin_chunks.clear()
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origin_chunks.append(d)
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sign = 1
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break
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if req.get("id"):
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if sign == 0:
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return get_error_data_result(f"Can't find this chunk {req.get('id')}")
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for chunk in origin_chunks:
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key_mapping = {
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"chunk_id": "id",
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"content_with_weight": "content",
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"doc_id": "document_id",
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"important_kwd": "important_keywords",
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"img_id": "image_id",
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}
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renamed_chunk = {}
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for key, value in chunk.items():
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new_key = key_mapping.get(key, key)
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renamed_chunk[new_key] = value
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res["chunks"].append(renamed_chunk)
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return get_result(data=res)
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origin_chunks.append(d)
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##rename keys
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for chunk in origin_chunks:
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key_mapping = {
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"chunk_id": "id",
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"content_with_weight": "content",
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"doc_id": "document_id",
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"important_kwd": "important_keywords",
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"img_id": "image_id",
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}
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renamed_chunk = {}
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for key, value in chunk.items():
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new_key = key_mapping.get(key, key)
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renamed_chunk[new_key] = value
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res["chunks"].append(renamed_chunk)
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return get_result(data=res)
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except Exception as e:
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if str(e).find("not_found") > 0:
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return get_result(retmsg=f'No chunk found!',
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retcode=RetCode.DATA_ERROR)
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return server_error_response(e)
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@manager.route('/dataset/<dataset_id>/document/<document_id>/chunk', methods=['POST'])
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@ -374,6 +378,9 @@ def create(tenant_id,dataset_id,document_id):
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req = request.json
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if not req.get("content"):
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return get_error_data_result(retmsg="`content` is required")
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if "important_keywords" in req:
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if type(req["important_keywords"]) != list:
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return get_error_data_result("`important_keywords` is required to be a list")
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md5 = hashlib.md5()
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md5.update((req["content"] + document_id).encode("utf-8"))
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@ -381,8 +388,8 @@ def create(tenant_id,dataset_id,document_id):
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d = {"id": chunk_id, "content_ltks": rag_tokenizer.tokenize(req["content"]),
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"content_with_weight": req["content"]}
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d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
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d["important_kwd"] = req.get("important_kwd", [])
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d["important_tks"] = rag_tokenizer.tokenize(" ".join(req.get("important_kwd", [])))
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d["important_kwd"] = req.get("important_keywords", [])
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d["important_tks"] = rag_tokenizer.tokenize(" ".join(req.get("important_keywords", [])))
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d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
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d["create_timestamp_flt"] = datetime.datetime.now().timestamp()
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d["kb_id"] = [doc.kb_id]
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@ -432,12 +439,12 @@ def rm_chunk(tenant_id,dataset_id,document_id):
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req = request.json
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if not req.get("chunk_ids"):
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return get_error_data_result("`chunk_ids` is required")
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query = {
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"doc_ids": [doc.id], "page": 1, "size": 1024, "question": "", "sort": True}
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sres = retrievaler.search(query, search.index_name(tenant_id), highlight=True)
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for chunk_id in req.get("chunk_ids"):
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res = ELASTICSEARCH.get(
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chunk_id, search.index_name(
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tenant_id))
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if not res.get("found"):
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return server_error_response(f"Chunk {chunk_id} not found")
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if chunk_id not in sres.ids:
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return get_error_data_result(f"Chunk {chunk_id} not found")
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if not ELASTICSEARCH.deleteByQuery(
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Q("ids", values=req["chunk_ids"]), search.index_name(tenant_id)):
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return get_error_data_result(retmsg="Index updating failure")
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@ -451,24 +458,36 @@ def rm_chunk(tenant_id,dataset_id,document_id):
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@manager.route('/dataset/<dataset_id>/document/<document_id>/chunk/<chunk_id>', methods=['PUT'])
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@token_required
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def set(tenant_id,dataset_id,document_id,chunk_id):
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res = ELASTICSEARCH.get(
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try:
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res = ELASTICSEARCH.get(
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chunk_id, search.index_name(
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tenant_id))
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if not res.get("found"):
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return get_error_data_result(f"Chunk {chunk_id} not found")
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except Exception as e:
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return get_error_data_result(f"Can't find this chunk {chunk_id}")
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if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
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return get_error_data_result(retmsg=f"You don't own the dataset {dataset_id}.")
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doc = DocumentService.query(id=document_id, kb_id=dataset_id)
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if not doc:
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return get_error_data_result(retmsg=f"You don't own the document {document_id}.")
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doc = doc[0]
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query = {
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"doc_ids": [document_id], "page": 1, "size": 1024, "question": "", "sort": True
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}
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sres = retrievaler.search(query, search.index_name(tenant_id), highlight=True)
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if chunk_id not in sres.ids:
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return get_error_data_result(f"You don't own the chunk {chunk_id}")
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req = request.json
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content=res["_source"].get("content_with_weight")
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d = {
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"id": chunk_id,
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"content_with_weight": req.get("content",res.get["content_with_weight"])}
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d["content_ltks"] = rag_tokenizer.tokenize(req["content"])
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"content_with_weight": req.get("content",content)}
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d["content_ltks"] = rag_tokenizer.tokenize(d["content_with_weight"])
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d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
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d["important_kwd"] = req.get("important_keywords",[])
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d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_keywords"]))
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if "important_keywords" in req:
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if type(req["important_keywords"]) != list:
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return get_error_data_result("`important_keywords` is required to be a list")
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d["important_kwd"] = req.get("important_keywords")
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d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_keywords"]))
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if "available" in req:
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d["available_int"] = req["available"]
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embd_id = DocumentService.get_embd_id(document_id)
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@ -478,7 +497,7 @@ def set(tenant_id,dataset_id,document_id,chunk_id):
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arr = [
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t for t in re.split(
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r"[\n\t]",
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req["content"]) if len(t) > 1]
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d["content_with_weight"]) if len(t) > 1]
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if len(arr) != 2:
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return get_error_data_result(
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retmsg="Q&A must be separated by TAB/ENTER key.")
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@ -486,7 +505,7 @@ def set(tenant_id,dataset_id,document_id,chunk_id):
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d = beAdoc(d, arr[0], arr[1], not any(
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[rag_tokenizer.is_chinese(t) for t in q + a]))
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v, c = embd_mdl.encode([doc.name, req["content"]])
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v, c = embd_mdl.encode([doc.name, d["content_with_weight"]])
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v = 0.1 * v[0] + 0.9 * v[1] if doc.parser_id != ParserType.QA else v[1]
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d["q_%d_vec" % len(v)] = v.tolist()
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ELASTICSEARCH.upsert([d], search.index_name(tenant_id))
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@ -505,7 +524,7 @@ def retrieval_test(tenant_id):
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for id in kb_id:
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if not KnowledgebaseService.query(id=id,tenant_id=tenant_id):
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return get_error_data_result(f"You don't own the dataset {id}.")
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if "question" not in req_json:
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if "question" not in req:
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return get_error_data_result("`question` is required.")
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page = int(req.get("offset", 1))
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size = int(req.get("limit", 30))
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@ -24,10 +24,9 @@ from api.utils import get_uuid
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from api.utils.api_utils import get_error_data_result
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from api.utils.api_utils import get_result, token_required
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@manager.route('/chat/<chat_id>/session', methods=['POST'])
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@token_required
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def create(tenant_id, chat_id):
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def create(tenant_id,chat_id):
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req = request.json
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req["dialog_id"] = chat_id
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dia = DialogService.query(tenant_id=tenant_id, id=req["dialog_id"], status=StatusEnum.VALID.value)
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@ -51,14 +50,13 @@ def create(tenant_id, chat_id):
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del conv["reference"]
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return get_result(data=conv)
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@manager.route('/chat/<chat_id>/session/<session_id>', methods=['PUT'])
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@token_required
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def update(tenant_id, chat_id, session_id):
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def update(tenant_id,chat_id,session_id):
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req = request.json
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req["dialog_id"] = chat_id
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conv_id = session_id
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conv = ConversationService.query(id=conv_id, dialog_id=chat_id)
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conv = ConversationService.query(id=conv_id,dialog_id=chat_id)
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if not conv:
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return get_error_data_result(retmsg="Session does not exist")
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if not DialogService.query(id=chat_id, tenant_id=tenant_id, status=StatusEnum.VALID.value):
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@ -74,16 +72,30 @@ def update(tenant_id, chat_id, session_id):
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return get_result()
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@manager.route('/chat/<chat_id>/session/<session_id>/completion', methods=['POST'])
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@manager.route('/chat/<chat_id>/completion', methods=['POST'])
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@token_required
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def completion(tenant_id, chat_id, session_id):
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def completion(tenant_id,chat_id):
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req = request.json
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# req = {"conversation_id": "9aaaca4c11d311efa461fa163e197198", "messages": [
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# {"role": "user", "content": "上海有吗？"}
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# ]}
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if not req.get("session_id"):
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conv = {
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"id": get_uuid(),
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"dialog_id": chat_id,
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"name": req.get("name", "New session"),
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"message": [{"role": "assistant", "content": "Hi! I am your assistant，can I help you?"}]
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}
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if not conv.get("name"):
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return get_error_data_result(retmsg="Name can not be empty.")
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ConversationService.save(**conv)
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e, conv = ConversationService.get_by_id(conv["id"])
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session_id=conv.id
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else:
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session_id = req.get("session_id")
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if not req.get("question"):
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return get_error_data_result(retmsg="Please input your question.")
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conv = ConversationService.query(id=session_id, dialog_id=chat_id)
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conv = ConversationService.query(id=session_id,dialog_id=chat_id)
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if not conv:
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return get_error_data_result(retmsg="Session does not exist")
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conv = conv[0]
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@ -117,17 +129,18 @@ def completion(tenant_id, chat_id, session_id):
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conv.message[-1] = {"role": "assistant", "content": ans["answer"],
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"id": message_id, "prompt": ans.get("prompt", "")}
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ans["id"] = message_id
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ans["session_id"]=session_id
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def stream():
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nonlocal dia, msg, req, conv
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try:
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for ans in chat(dia, msg, **req):
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fillin_conv(ans)
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yield "data:" + json.dumps({"code": 0, "data": ans}, ensure_ascii=False) + "\n\n"
|
||||
yield "data:" + json.dumps({"code": 0, "data": ans}, ensure_ascii=False) + "\n\n"
|
||||
ConversationService.update_by_id(conv.id, conv.to_dict())
|
||||
except Exception as e:
|
||||
yield "data:" + json.dumps({"code": 500, "message": str(e),
|
||||
"data": {"answer": "**ERROR**: " + str(e), "reference": []}},
|
||||
"data": {"answer": "**ERROR**: " + str(e),"reference": []}},
|
||||
ensure_ascii=False) + "\n\n"
|
||||
yield "data:" + json.dumps({"code": 0, "data": True}, ensure_ascii=False) + "\n\n"
|
||||
|
||||
@ -148,15 +161,14 @@ def completion(tenant_id, chat_id, session_id):
|
||||
break
|
||||
return get_result(data=answer)
|
||||
|
||||
|
||||
@manager.route('/chat/<chat_id>/session', methods=['GET'])
|
||||
@token_required
|
||||
def list(chat_id, tenant_id):
|
||||
def list(chat_id,tenant_id):
|
||||
if not DialogService.query(tenant_id=tenant_id, id=chat_id, status=StatusEnum.VALID.value):
|
||||
return get_error_data_result(retmsg=f"You don't own the assistant {chat_id}.")
|
||||
id = request.args.get("id")
|
||||
name = request.args.get("name")
|
||||
session = ConversationService.query(id=id, name=name, dialog_id=chat_id)
|
||||
session = ConversationService.query(id=id,name=name,dialog_id=chat_id)
|
||||
if not session:
|
||||
return get_error_data_result(retmsg="The session doesn't exist")
|
||||
page_number = int(request.args.get("page", 1))
|
||||
@ -166,7 +178,7 @@ def list(chat_id, tenant_id):
|
||||
desc = False
|
||||
else:
|
||||
desc = True
|
||||
convs = ConversationService.get_list(chat_id, page_number, items_per_page, orderby, desc, id, name)
|
||||
convs = ConversationService.get_list(chat_id,page_number,items_per_page,orderby,desc,id,name)
|
||||
if not convs:
|
||||
return get_result(data=[])
|
||||
for conv in convs:
|
||||
@ -201,17 +213,16 @@ def list(chat_id, tenant_id):
|
||||
del conv["reference"]
|
||||
return get_result(data=convs)
|
||||
|
||||
|
||||
@manager.route('/chat/<chat_id>/session', methods=["DELETE"])
|
||||
@token_required
|
||||
def delete(tenant_id, chat_id):
|
||||
def delete(tenant_id,chat_id):
|
||||
if not DialogService.query(id=chat_id, tenant_id=tenant_id, status=StatusEnum.VALID.value):
|
||||
return get_error_data_result(retmsg="You don't own the chat")
|
||||
ids = request.json.get("ids")
|
||||
if not ids:
|
||||
return get_error_data_result(retmsg="`ids` is required in deleting operation")
|
||||
for id in ids:
|
||||
conv = ConversationService.query(id=id, dialog_id=chat_id)
|
||||
conv = ConversationService.query(id=id,dialog_id=chat_id)
|
||||
if not conv:
|
||||
return get_error_data_result(retmsg="The chat doesn't own the session")
|
||||
ConversationService.delete_by_id(id)
|
||||
|
||||
@ -61,14 +61,13 @@ class DocumentService(CommonService):
|
||||
docs = docs.where(
|
||||
fn.LOWER(cls.model.name).contains(keywords.lower())
|
||||
)
|
||||
count = docs.count()
|
||||
if desc:
|
||||
docs = docs.order_by(cls.model.getter_by(orderby).desc())
|
||||
else:
|
||||
docs = docs.order_by(cls.model.getter_by(orderby).asc())
|
||||
|
||||
docs = docs.paginate(page_number, items_per_page)
|
||||
|
||||
count = docs.count()
|
||||
return list(docs.dicts()), count
|
||||
|
||||
|
||||
|
||||
570
api/http_api.md
570
api/http_api.md
@ -432,18 +432,71 @@ The error response includes a JSON object like the following:
|
||||
}
|
||||
```
|
||||
|
||||
## Delete files from a dataset
|
||||
|
||||
**DELETE** `/api/v1/dataset/{dataset_id}/document `
|
||||
|
||||
Delete files from a dataset
|
||||
|
||||
### Request
|
||||
|
||||
- Method: DELETE
|
||||
- URL: `http://{address}/api/v1/dataset/{dataset_id}/document`
|
||||
- Headers:
|
||||
- 'Content-Type: application/json'
|
||||
- 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
- Body:
|
||||
- `ids`:List[str]
|
||||
#### Request example
|
||||
|
||||
```bash
|
||||
curl --request DELETE \
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/document \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: {YOUR ACCESS TOKEN}' \
|
||||
--data '{
|
||||
"ids": ["id_1","id_2"]
|
||||
}'
|
||||
```
|
||||
|
||||
#### Request parameters
|
||||
|
||||
- `"ids"`: (*Body parameter*)
|
||||
The ids of teh documents to be deleted
|
||||
### Response
|
||||
|
||||
The successful response includes a JSON object like the following:
|
||||
|
||||
```json
|
||||
{
|
||||
"code": 0
|
||||
}.
|
||||
```
|
||||
|
||||
- `"error_code"`: `integer`
|
||||
`0`: The operation succeeds.
|
||||
|
||||
|
||||
The error response includes a JSON object like the following:
|
||||
|
||||
```json
|
||||
{
|
||||
"code": 102,
|
||||
"message": "You do not own the dataset 7898da028a0511efbf750242ac1220005."
|
||||
}
|
||||
```
|
||||
|
||||
## Download a file from a dataset
|
||||
|
||||
**GET** `/api/v1/dataset/{dataset_id}/document/{document_id}`
|
||||
|
||||
Downloads files from a dataset.
|
||||
Downloads a file from a dataset.
|
||||
|
||||
### Request
|
||||
|
||||
- Method: GET
|
||||
- URL: `/api/v1/dataset/{dataset_id}/document/{document_id}`
|
||||
- URL: `http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}`
|
||||
- Headers:
|
||||
- `content-Type: application/json`
|
||||
- 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
- Output:
|
||||
- '{FILE_NAME}'
|
||||
@ -451,10 +504,9 @@ Downloads files from a dataset.
|
||||
|
||||
```bash
|
||||
curl --request GET \
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/document/{documents_id} \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
--output '{FILE_NAME}'
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id} \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \
|
||||
--output ./ragflow.txt
|
||||
```
|
||||
|
||||
#### Request parameters
|
||||
@ -466,7 +518,7 @@ curl --request GET \
|
||||
|
||||
### Response
|
||||
|
||||
The successful response includes a JSON object like the following:
|
||||
The successful response includes a text object like the following:
|
||||
|
||||
```text
|
||||
test_2.
|
||||
@ -596,92 +648,39 @@ Update a file in a dataset
|
||||
- Headers:
|
||||
- `content-Type: application/json`
|
||||
- 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
|
||||
- Body:
|
||||
- `name`:`string`
|
||||
- `parser_method`:`string`
|
||||
- `parser_config`:`dict`
|
||||
#### Request example
|
||||
|
||||
```bash
|
||||
curl --request PUT \
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id} \
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/info/{document_id} \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS TOKEN}' \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"name": "manual.txt",
|
||||
"thumbnail": null,
|
||||
"knowledgebase_id": "779333c0758611ef910f0242ac120004",
|
||||
"parser_method": "manual",
|
||||
"parser_config": {"chunk_token_count": 128, "delimiter": "\n!?。；！？", "layout_recognize": true, "task_page_size": 12},
|
||||
"source_type": "local", "type": "doc",
|
||||
"created_by": "134408906b6811efbcd20242ac120005",
|
||||
"size": 0, "token_count": 0, "chunk_count": 0,
|
||||
"progress": 0.0,
|
||||
"progress_msg": "",
|
||||
"process_begin_at": null,
|
||||
"process_duration": 0.0
|
||||
"parser_config": {"chunk_token_count": 128, "delimiter": "\n!?。；！？", "layout_recognize": true, "task_page_size": 12}
|
||||
}'
|
||||
|
||||
```
|
||||
|
||||
#### Request parameters
|
||||
|
||||
- `"thumbnail"`: (*Body parameter*)
|
||||
Thumbnail image of the document.
|
||||
- `""`
|
||||
|
||||
- `"knowledgebase_id"`: (*Body parameter*)
|
||||
Knowledge base ID related to the document.
|
||||
- `""`
|
||||
|
||||
- `"parser_method"`: (*Body parameter*)
|
||||
Method used to parse the document.
|
||||
- `""`
|
||||
|
||||
|
||||
- `"parser_config"`: (*Body parameter*)
|
||||
Configuration object for the parser.
|
||||
- If the value is `None`, a dictionary with default values will be generated.
|
||||
|
||||
- `"source_type"`: (*Body parameter*)
|
||||
Source type of the document.
|
||||
- `""`
|
||||
|
||||
- `"type"`: (*Body parameter*)
|
||||
Type or category of the document.
|
||||
- `""`
|
||||
|
||||
- `"created_by"`: (*Body parameter*)
|
||||
Creator of the document.
|
||||
- `""`
|
||||
|
||||
- `"name"`: (*Body parameter*)
|
||||
Name or title of the document.
|
||||
- `""`
|
||||
|
||||
- `"size"`: (*Body parameter*)
|
||||
Size of the document in bytes or some other unit.
|
||||
- `0`
|
||||
|
||||
- `"token_count"`: (*Body parameter*)
|
||||
Number of tokens in the document.
|
||||
- `0`
|
||||
|
||||
- `"chunk_count"`: (*Body parameter*)
|
||||
Number of chunks the document is split into.
|
||||
- `0`
|
||||
|
||||
- `"progress"`: (*Body parameter*)
|
||||
Current processing progress as a percentage.
|
||||
- `0.0`
|
||||
|
||||
- `"progress_msg"`: (*Body parameter*)
|
||||
Message indicating current progress status.
|
||||
- `""`
|
||||
|
||||
- `"process_begin_at"`: (*Body parameter*)
|
||||
Start time of the document processing.
|
||||
- `None`
|
||||
|
||||
- `"process_duration"`: (*Body parameter*)
|
||||
Duration of the processing in seconds or minutes.
|
||||
- `0.0`
|
||||
|
||||
|
||||
### Response
|
||||
@ -712,34 +711,34 @@ Parse files into chunks in a dataset
|
||||
### Request
|
||||
|
||||
- Method: POST
|
||||
- URL: `/api/v1/dataset/{dataset_id}/chunk`
|
||||
- URL: `http://{address}/api/v1/dataset/{dataset_id}/chunk `
|
||||
- Headers:
|
||||
- `content-Type: application/json`
|
||||
- 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
- Body:
|
||||
- `document_ids`:List[str]
|
||||
|
||||
#### Request example
|
||||
|
||||
```shell
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/chunk \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
--raw '{
|
||||
"documents": ["f6b170ac758811efa0660242ac120004", "97ad64b6759811ef9fc30242ac120004"]
|
||||
}'
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/chunk \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \
|
||||
--data '{"document_ids": ["97a5f1c2759811efaa500242ac120004","97ad64b6759811ef9fc30242ac120004"]}'
|
||||
```
|
||||
|
||||
#### Request parameters
|
||||
|
||||
- `"dataset_id"`: (*Path parameter*)
|
||||
- `"documents"`: (*Body parameter*)
|
||||
- Documents to parse
|
||||
- `"document_ids"`:(*Body parameter*)
|
||||
The ids of the documents to be parsed
|
||||
|
||||
### Response
|
||||
|
||||
The successful response includes a JSON object like the following:
|
||||
|
||||
```shell
|
||||
```json
|
||||
{
|
||||
"code": 0
|
||||
}
|
||||
@ -747,10 +746,10 @@ The successful response includes a JSON object like the following:
|
||||
|
||||
The error response includes a JSON object like the following:
|
||||
|
||||
```shell
|
||||
```json
|
||||
{
|
||||
"code": 3016,
|
||||
"message": "Can't connect database"
|
||||
"code": 102,
|
||||
"message": "`document_ids` is required"
|
||||
}
|
||||
```
|
||||
|
||||
@ -762,35 +761,35 @@ Stop file parsing
|
||||
|
||||
### Request
|
||||
|
||||
- Method: POST
|
||||
- URL: `/api/v1/dataset/{dataset_id}/chunk`
|
||||
- Method: DELETE
|
||||
- URL: `http://{address}/api/v1/dataset/{dataset_id}/chunk`
|
||||
- Headers:
|
||||
- `content-Type: application/json`
|
||||
- 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
|
||||
- Body:
|
||||
- `document_ids`:List[str]
|
||||
#### Request example
|
||||
|
||||
```shell
|
||||
```bash
|
||||
curl --request DELETE \
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/chunk \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
--raw '{
|
||||
"documents": ["f6b170ac758811efa0660242ac120004", "97ad64b6759811ef9fc30242ac120004"]
|
||||
}'
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/chunk \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \
|
||||
--data '{"document_ids": ["97a5f1c2759811efaa500242ac120004","97ad64b6759811ef9fc30242ac120004"]}'
|
||||
```
|
||||
|
||||
#### Request parameters
|
||||
|
||||
- `"dataset_id"`: (*Path parameter*)
|
||||
- `"documents"`: (*Body parameter*)
|
||||
- Documents to stop parsing
|
||||
- `"document_ids"`:(*Body parameter*)
|
||||
The ids of the documents to be parsed
|
||||
|
||||
|
||||
### Response
|
||||
|
||||
The successful response includes a JSON object like the following:
|
||||
|
||||
```shell
|
||||
```json
|
||||
{
|
||||
"code": 0
|
||||
}
|
||||
@ -798,104 +797,98 @@ The successful response includes a JSON object like the following:
|
||||
|
||||
The error response includes a JSON object like the following:
|
||||
|
||||
```shell
|
||||
```json
|
||||
{
|
||||
"code": 3016,
|
||||
"message": "Can't connect database"
|
||||
"code": 102,
|
||||
"message": "`document_ids` is required"
|
||||
}
|
||||
```
|
||||
|
||||
## Get document chunk list
|
||||
|
||||
**GET** `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk`
|
||||
**GET** `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk?keywords={keywords}&offset={offset}&limit={limit}&id={id}`
|
||||
|
||||
Get document chunk list
|
||||
|
||||
### Request
|
||||
|
||||
- Method: GET
|
||||
- URL: `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk`
|
||||
- URL: `http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk?keywords={keywords}&offset={offset}&limit={limit}&id={id}`
|
||||
- Headers:
|
||||
- `content-Type: application/json`
|
||||
- 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
|
||||
#### Request example
|
||||
|
||||
```shell
|
||||
```bash
|
||||
curl --request GET \
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk?keywords={keywords}&offset={offset}&limit={limit}&id={id} \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
```
|
||||
|
||||
#### Request parameters
|
||||
|
||||
- `"dataset_id"`: (*Path parameter*)
|
||||
- `"document_id"`: (*Path parameter*)
|
||||
|
||||
- `"offset"`(*Filter parameter*)
|
||||
The beginning number of records for paging.
|
||||
- `"keywords"`(*Filter parameter*)
|
||||
List chunks whose name has the given keywords
|
||||
- `"limit"`(*Filter parameter*)
|
||||
Records number to return
|
||||
- `"id"`(*Filter parameter*)
|
||||
The id of chunk to be got
|
||||
### Response
|
||||
|
||||
The successful response includes a JSON object like the following:
|
||||
|
||||
```shell
|
||||
```json
|
||||
{
|
||||
"code": 0
|
||||
"code": 0,
|
||||
"data": {
|
||||
"chunks": [
|
||||
{
|
||||
"available_int": 1,
|
||||
"content": "<em>advantag</em>of ragflow increas accuraci and relev:by incorpor retriev inform , ragflow can gener respons that are more accur",
|
||||
"document_keyword": "ragflow_test.txt",
|
||||
"document_id": "77df9ef4759a11ef8bdd0242ac120004",
|
||||
"id": "4ab8c77cfac1a829c8d5ed022a0808c0",
|
||||
"image_id": "",
|
||||
"important_keywords": [],
|
||||
"positions": [
|
||||
""
|
||||
]
|
||||
}
|
||||
],
|
||||
"chunks": [],
|
||||
"doc": {
|
||||
"chunk_count": 5,
|
||||
"create_date": "Wed, 18 Sep 2024 08:46:16 GMT",
|
||||
"create_time": 1726649176833,
|
||||
"created_by": "134408906b6811efbcd20242ac120005",
|
||||
"id": "77df9ef4759a11ef8bdd0242ac120004",
|
||||
"knowledgebase_id": "77d9d24e759a11ef880c0242ac120004",
|
||||
"location": "ragflow_test.txt",
|
||||
"name": "ragflow_test.txt",
|
||||
"chunk_num": 0,
|
||||
"create_date": "Sun, 29 Sep 2024 03:47:29 GMT",
|
||||
"create_time": 1727581649216,
|
||||
"created_by": "69736c5e723611efb51b0242ac120007",
|
||||
"id": "8cb781ec7e1511ef98ac0242ac120006",
|
||||
"kb_id": "c7ee74067a2c11efb21c0242ac120006",
|
||||
"location": "明天的天气是晴天.txt",
|
||||
"name": "明天的天气是晴天.txt",
|
||||
"parser_config": {
|
||||
"chunk_token_count": 128,
|
||||
"delimiter": "\n!?。；！？",
|
||||
"layout_recognize": true,
|
||||
"task_page_size": 12
|
||||
"pages": [
|
||||
[
|
||||
1,
|
||||
1000000
|
||||
]
|
||||
]
|
||||
},
|
||||
"parser_method": "naive",
|
||||
"process_begin_at": "Wed, 18 Sep 2024 08:46:16 GMT",
|
||||
"process_duation": 7.3213,
|
||||
"progress": 1.0,
|
||||
"progress_msg": "\nTask has been received.\nStart to parse.\nFinish parsing.\nFinished slicing files(5). Start to embedding the content.\nFinished embedding(6.16)! Start to build index!\nDone!",
|
||||
"run": "3",
|
||||
"size": 4209,
|
||||
"parser_id": "naive",
|
||||
"process_begin_at": "Tue, 15 Oct 2024 10:23:51 GMT",
|
||||
"process_duation": 1435.37,
|
||||
"progress": 0.0370833,
|
||||
"progress_msg": "\nTask has been received.",
|
||||
"run": "1",
|
||||
"size": 24,
|
||||
"source_type": "local",
|
||||
"status": "1",
|
||||
"thumbnail": null,
|
||||
"token_count": 746,
|
||||
"token_num": 0,
|
||||
"type": "doc",
|
||||
"update_date": "Wed, 18 Sep 2024 08:46:23 GMT",
|
||||
"update_time": 1726649183321
|
||||
"update_date": "Tue, 15 Oct 2024 10:47:46 GMT",
|
||||
"update_time": 1728989266371
|
||||
},
|
||||
"total": 1
|
||||
},
|
||||
"total": 0
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The error response includes a JSON object like the following:
|
||||
|
||||
```shell
|
||||
```json
|
||||
{
|
||||
"code": 3016,
|
||||
"message": "Can't connect database"
|
||||
"code": 102,
|
||||
"message": "You don't own the document 5c5999ec7be811ef9cab0242ac12000e5."
|
||||
}
|
||||
```
|
||||
|
||||
@ -908,55 +901,96 @@ Delete document chunks
|
||||
### Request
|
||||
|
||||
- Method: DELETE
|
||||
- URL: `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk`
|
||||
- URL: `http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk`
|
||||
- Headers:
|
||||
- `content-Type: application/json`
|
||||
- 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
- Body:
|
||||
- `chunk_ids`:List[str]
|
||||
|
||||
#### Request example
|
||||
|
||||
```shell
|
||||
```bash
|
||||
curl --request DELETE \
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
--raw '{
|
||||
"chunks": ["f6b170ac758811efa0660242ac120004", "97ad64b6759811ef9fc30242ac120004"]
|
||||
}'
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \
|
||||
--data '{
|
||||
"chunk_ids": ["test_1", "test_2"]
|
||||
}'
|
||||
```
|
||||
#### Request parameters
|
||||
|
||||
- `"chunk_ids"`:(*Body parameter*)
|
||||
The chunks of the document to be deleted
|
||||
|
||||
### Response
|
||||
Success
|
||||
```json
|
||||
{
|
||||
"code": 0
|
||||
}
|
||||
```
|
||||
Error
|
||||
```json
|
||||
{
|
||||
"code": 102,
|
||||
"message": "`chunk_ids` is required"
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
## Update document chunk
|
||||
|
||||
**PUT** `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk`
|
||||
**PUT** `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk/{chunk_id}`
|
||||
|
||||
Update document chunk
|
||||
|
||||
### Request
|
||||
|
||||
- Method: PUT
|
||||
- URL: `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk`
|
||||
- URL: `http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk/{chunk_id}`
|
||||
- Headers:
|
||||
- `content-Type: application/json`
|
||||
- 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
|
||||
- Body:
|
||||
- `content`:str
|
||||
- `important_keywords`:str
|
||||
- `available`:int
|
||||
#### Request example
|
||||
|
||||
```shell
|
||||
```bash
|
||||
curl --request PUT \
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
--raw '{
|
||||
"chunk_id": "d87fb0b7212c15c18d0831677552d7de",
|
||||
"knowledgebase_id": null,
|
||||
"name": "",
|
||||
"content": "ragflow123",
|
||||
"important_keywords": [],
|
||||
"document_id": "e6bbba92759511efaa900242ac120004",
|
||||
"status": "1"
|
||||
}'
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk/{chunk_id} \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: {YOUR_ACCESS_TOKEN}' \
|
||||
--data '{
|
||||
"content": "ragflow123",
|
||||
"important_keywords": [],
|
||||
}'
|
||||
```
|
||||
#### Request parameters
|
||||
- `"content"`:(*Body parameter*)
|
||||
Contains the main text or information of the chunk.
|
||||
- `"important_keywords"`:(*Body parameter*)
|
||||
list the key terms or phrases that are significant or central to the chunk's content.
|
||||
- `"available"`:(*Body parameter*)
|
||||
Indicating the availability status, 0 means unavailable and 1 means available.
|
||||
|
||||
### Response
|
||||
Success
|
||||
```json
|
||||
{
|
||||
"code": 0
|
||||
}
|
||||
```
|
||||
Error
|
||||
```json
|
||||
{
|
||||
"code": 102,
|
||||
"message": "Can't find this chunk 29a2d9987e16ba331fb4d7d30d99b71d2"
|
||||
}
|
||||
```
|
||||
## Insert document chunks
|
||||
|
||||
**POST** `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk`
|
||||
@ -966,50 +1000,187 @@ Insert document chunks
|
||||
### Request
|
||||
|
||||
- Method: POST
|
||||
- URL: `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk`
|
||||
- URL: `http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk`
|
||||
- Headers:
|
||||
- `content-Type: application/json`
|
||||
- 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
|
||||
- Body:
|
||||
- `content`: str
|
||||
- `important_keywords`:List[str]
|
||||
#### Request example
|
||||
|
||||
```shell
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
--raw '{
|
||||
"document_id": "97ad64b6759811ef9fc30242ac120004",
|
||||
"content": ["ragflow content", "ragflow content"]
|
||||
}'
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \
|
||||
--data '{
|
||||
"content": "ragflow content"
|
||||
}'
|
||||
```
|
||||
#### Request parameters
|
||||
- `content`:(*Body parameter*)
|
||||
Contains the main text or information of the chunk.
|
||||
- `important_keywords`(*Body parameter*)
|
||||
list the key terms or phrases that are significant or central to the chunk's content.
|
||||
|
||||
### Response
|
||||
Success
|
||||
```json
|
||||
{
|
||||
"code": 0,
|
||||
"data": {
|
||||
"chunk": {
|
||||
"content": "ragflow content",
|
||||
"create_time": "2024-10-16 08:05:04",
|
||||
"create_timestamp": 1729065904.581025,
|
||||
"dataset_id": [
|
||||
"c7ee74067a2c11efb21c0242ac120006"
|
||||
],
|
||||
"document_id": "5c5999ec7be811ef9cab0242ac120005",
|
||||
"id": "d78435d142bd5cf6704da62c778795c5",
|
||||
"important_keywords": []
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Error
|
||||
```json
|
||||
{
|
||||
"code": 102,
|
||||
"message": "`content` is required"
|
||||
}
|
||||
```
|
||||
## Dataset retrieval test
|
||||
|
||||
**GET** `/api/v1/dataset/{dataset_id}/retrieval`
|
||||
**GET** `/api/v1/retrieval`
|
||||
|
||||
Retrieval test of a dataset
|
||||
|
||||
### Request
|
||||
|
||||
- Method: GET
|
||||
- URL: `/api/v1/dataset/{dataset_id}/retrieval`
|
||||
- Method: POST
|
||||
- URL: `http://{address}/api/v1/retrieval`
|
||||
- Headers:
|
||||
- `content-Type: application/json`
|
||||
- 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
|
||||
- Body:
|
||||
- `question`: str
|
||||
- `datasets`: List[str]
|
||||
- `documents`: List[str]
|
||||
- `offset`: int
|
||||
- `limit`: int
|
||||
- `similarity_threshold`: float
|
||||
- `vector_similarity_weight`: float
|
||||
- `top_k`: int
|
||||
- `rerank_id`: string
|
||||
- `keyword`: bool
|
||||
- `highlight`: bool
|
||||
#### Request example
|
||||
|
||||
```shell
|
||||
curl --request GET \
|
||||
--url http://{address}/api/v1/dataset/{dataset_id}/retrieval \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
--raw '{
|
||||
"query_text": "This is a cat."
|
||||
}'
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url http://{address}/api/v1/retrieval \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: {YOUR_ACCESS_TOKEN}' \
|
||||
--data '{
|
||||
"question": "What is advantage of ragflow?",
|
||||
"datasets": [
|
||||
"b2a62730759d11ef987d0242ac120004"
|
||||
],
|
||||
"documents": [
|
||||
"77df9ef4759a11ef8bdd0242ac120004"
|
||||
]
|
||||
}'
|
||||
```
|
||||
|
||||
#### Request parameter
|
||||
- `"question"`: (*Body parameter*)
|
||||
User's question, search keywords
|
||||
`""`
|
||||
- `"datasets"`: (*Body parameter*)
|
||||
The scope of datasets
|
||||
`None`
|
||||
- `"documents"`: (*Body parameter*)
|
||||
The scope of document. `None` means no limitation
|
||||
`None`
|
||||
- `"offset"`: (*Body parameter*)
|
||||
The beginning point of retrieved records
|
||||
`1`
|
||||
|
||||
- `"limit"`: (*Body parameter*)
|
||||
The maximum number of records needed to return
|
||||
`30`
|
||||
|
||||
- `"similarity_threshold"`: (*Body parameter*)
|
||||
The minimum similarity score
|
||||
`0.2`
|
||||
|
||||
- `"vector_similarity_weight"`: (*Body parameter*)
|
||||
The weight of vector cosine similarity, `1 - x` is the term similarity weight
|
||||
`0.3`
|
||||
|
||||
- `"top_k"`: (*Body parameter*)
|
||||
Number of records engaged in vector cosine computation
|
||||
`1024`
|
||||
|
||||
- `"rerank_id"`: (*Body parameter*)
|
||||
ID of the rerank model
|
||||
`None`
|
||||
|
||||
- `"keyword"`: (*Body parameter*)
|
||||
Whether keyword-based matching is enabled
|
||||
`False`
|
||||
|
||||
- `"highlight"`: (*Body parameter*)
|
||||
Whether to enable highlighting of matched terms in the results
|
||||
`False`
|
||||
### Response
|
||||
Success
|
||||
```json
|
||||
{
|
||||
"code": 0,
|
||||
"data": {
|
||||
"chunks": [
|
||||
{
|
||||
"content": "ragflow content",
|
||||
"content_ltks": "ragflow content",
|
||||
"document_id": "5c5999ec7be811ef9cab0242ac120005",
|
||||
"document_keyword": "1.txt",
|
||||
"highlight": "<em>ragflow</em> content",
|
||||
"id": "d78435d142bd5cf6704da62c778795c5",
|
||||
"img_id": "",
|
||||
"important_keywords": [
|
||||
""
|
||||
],
|
||||
"kb_id": "c7ee74067a2c11efb21c0242ac120006",
|
||||
"positions": [
|
||||
""
|
||||
],
|
||||
"similarity": 0.9669436601210759,
|
||||
"term_similarity": 1.0,
|
||||
"vector_similarity": 0.8898122004035864
|
||||
}
|
||||
],
|
||||
"doc_aggs": [
|
||||
{
|
||||
"count": 1,
|
||||
"doc_id": "5c5999ec7be811ef9cab0242ac120005",
|
||||
"doc_name": "1.txt"
|
||||
}
|
||||
],
|
||||
"total": 1
|
||||
}
|
||||
}
|
||||
```
|
||||
Error
|
||||
```json
|
||||
{
|
||||
"code": 102,
|
||||
"message": "`datasets` is required."
|
||||
}
|
||||
```
|
||||
## Create chat
|
||||
|
||||
**POST** `/api/v1/chat`
|
||||
@ -1708,26 +1879,27 @@ Error
|
||||
|
||||
## Chat with a chat session
|
||||
|
||||
**POST** `/api/v1/chat/{chat_id}/session/{session_id}/completion`
|
||||
**POST** `/api/v1/chat/{chat_id}/completion`
|
||||
|
||||
Chat with a chat session
|
||||
|
||||
### Request
|
||||
|
||||
- Method: POST
|
||||
- URL: `http://{address} /api/v1/chat/{chat_id}/session/{session_id}/completion`
|
||||
- URL: `http://{address} /api/v1/chat/{chat_id}/completion`
|
||||
- Headers:
|
||||
- `content-Type: application/json`
|
||||
- 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
|
||||
- Body:
|
||||
- `question`: string
|
||||
- `stream`: bool
|
||||
- `session_id`: str
|
||||
|
||||
|
||||
#### Request example
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url http://{address} /api/v1/chat/{chat_id}/session/{session_id}/completion \
|
||||
--url http://{address} /api/v1/chat/{chat_id}/completion \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \
|
||||
--data-binary '{
|
||||
@ -1743,6 +1915,8 @@ curl --request POST \
|
||||
- `stream`: (*Body Parameter*)
|
||||
The approach of streaming text generation.
|
||||
`False`
|
||||
- `session_id`: (*Body Parameter*)
|
||||
The id of session.If not provided, a new session will be generated.
|
||||
### Response
|
||||
Success
|
||||
```json
|
||||
|
||||
@ -244,42 +244,117 @@ File management inside knowledge base
|
||||
## Upload document
|
||||
|
||||
```python
|
||||
RAGFLOW.upload_document(ds:DataSet, name:str, blob:bytes)-> bool
|
||||
DataSet.upload_documents(document_list: List[dict])
|
||||
```
|
||||
|
||||
### Parameters
|
||||
|
||||
#### name
|
||||
|
||||
#### blob
|
||||
|
||||
#### document_list:`List[dict]`
|
||||
A list composed of dicts containing `name` and `blob`.
|
||||
|
||||
|
||||
### Returns
|
||||
no return
|
||||
|
||||
### Examples
|
||||
```python
|
||||
from ragflow import RAGFlow
|
||||
|
||||
rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
|
||||
ds = rag.create_dataset(name="kb_1")
|
||||
ds.upload_documents([{name="1.txt", blob="123"}, ...] }
|
||||
```
|
||||
---
|
||||
|
||||
## Update document
|
||||
|
||||
```python
|
||||
Document.update(update_message:dict)
|
||||
```
|
||||
|
||||
### Parameters
|
||||
|
||||
#### update_message:`dict`
|
||||
only `name`,`parser_config`,`parser_method` can be changed
|
||||
|
||||
### Returns
|
||||
|
||||
no return
|
||||
|
||||
### Examples
|
||||
|
||||
```python
|
||||
from ragflow import RAGFlow
|
||||
|
||||
rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
|
||||
ds=rag.list_datasets(id='id')
|
||||
ds=ds[0]
|
||||
doc = ds.list_documents(id="wdfxb5t547d")
|
||||
doc = doc[0]
|
||||
doc.update([{"parser_method": "manual"...}])
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Retrieve document
|
||||
## Download document
|
||||
|
||||
```python
|
||||
RAGFlow.get_document(id:str=None,name:str=None) -> Document
|
||||
Document.download() -> bytes
|
||||
```
|
||||
|
||||
### Returns
|
||||
|
||||
bytes of the document.
|
||||
|
||||
### Examples
|
||||
|
||||
```python
|
||||
from ragflow import RAGFlow
|
||||
|
||||
rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
|
||||
ds=rag.list_datasets(id="id")
|
||||
ds=ds[0]
|
||||
doc = ds.list_documents(id="wdfxb5t547d")
|
||||
doc = doc[0]
|
||||
open("~/ragflow.txt", "wb+").write(doc.download())
|
||||
print(doc)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## List documents
|
||||
|
||||
```python
|
||||
Dataset.list_documents(id:str =None, keywords: str=None, offset: int=0, limit:int = 1024,order_by:str = "create_time", desc: bool = True) -> List[Document]
|
||||
```
|
||||
|
||||
### Parameters
|
||||
|
||||
#### id: `str`, *Required*
|
||||
#### id: `str`
|
||||
|
||||
ID of the document to retrieve.
|
||||
The id of the document to be got
|
||||
|
||||
#### name: `str`
|
||||
#### keywords: `str`
|
||||
|
||||
Name or title of the document.
|
||||
List documents whose name has the given keywords. Defaults to `None`.
|
||||
|
||||
#### offset: `int`
|
||||
|
||||
The beginning number of records for paging. Defaults to `0`.
|
||||
|
||||
#### limit: `int`
|
||||
|
||||
Records number to return, -1 means all of them. Records number to return, -1 means all of them.
|
||||
|
||||
#### orderby: `str`
|
||||
The field by which the records should be sorted. This specifies the attribute or column used to order the results.
|
||||
|
||||
#### desc:`bool`
|
||||
A boolean flag indicating whether the sorting should be in descending order.
|
||||
### Returns
|
||||
|
||||
List[Document]
|
||||
|
||||
A document object containing the following attributes:
|
||||
|
||||
#### id: `str`
|
||||
@ -352,98 +427,14 @@ Duration of the processing in seconds or minutes. Defaults to `0.0`.
|
||||
```python
|
||||
from ragflow import RAGFlow
|
||||
|
||||
rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
|
||||
doc = rag.get_document(id="wdfxb5t547d",name='testdocument.txt')
|
||||
print(doc)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Save document settings
|
||||
|
||||
```python
|
||||
Document.save() -> bool
|
||||
```
|
||||
|
||||
### Returns
|
||||
|
||||
bool
|
||||
|
||||
### Examples
|
||||
|
||||
```python
|
||||
from ragflow import RAGFlow
|
||||
|
||||
rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
|
||||
doc = rag.get_document(id="wdfxb5t547d")
|
||||
doc.parser_method= "manual"
|
||||
doc.save()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Download document
|
||||
|
||||
```python
|
||||
Document.download() -> bytes
|
||||
```
|
||||
|
||||
### Returns
|
||||
|
||||
bytes of the document.
|
||||
|
||||
### Examples
|
||||
|
||||
```python
|
||||
from ragflow import RAGFlow
|
||||
|
||||
rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
|
||||
doc = rag.get_document(id="wdfxb5t547d")
|
||||
open("~/ragflow.txt", "w+").write(doc.download())
|
||||
print(doc)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## List documents
|
||||
|
||||
```python
|
||||
Dataset.list_docs(keywords: str=None, offset: int=0, limit:int = -1) -> List[Document]
|
||||
```
|
||||
|
||||
### Parameters
|
||||
|
||||
#### keywords: `str`
|
||||
|
||||
List documents whose name has the given keywords. Defaults to `None`.
|
||||
|
||||
#### offset: `int`
|
||||
|
||||
The beginning number of records for paging. Defaults to `0`.
|
||||
|
||||
#### limit: `int`
|
||||
|
||||
Records number to return, -1 means all of them. Records number to return, -1 means all of them.
|
||||
|
||||
### Returns
|
||||
|
||||
List[Document]
|
||||
|
||||
### Examples
|
||||
|
||||
```python
|
||||
from ragflow import RAGFlow
|
||||
|
||||
rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
|
||||
ds = rag.create_dataset(name="kb_1")
|
||||
|
||||
filename1 = "~/ragflow.txt"
|
||||
rag.create_document(ds, name=filename1 , blob=open(filename1 , "rb").read())
|
||||
|
||||
filename2 = "~/infinity.txt"
|
||||
rag.create_document(ds, name=filename2 , blob=open(filename2 , "rb").read())
|
||||
|
||||
for d in ds.list_docs(keywords="rag", offset=0, limit=12):
|
||||
blob=open(filename1 , "rb").read()
|
||||
list_files=[{"name":filename1,"blob":blob}]
|
||||
ds.upload_documents(list_files)
|
||||
for d in ds.list_documents(keywords="rag", offset=0, limit=12):
|
||||
print(d)
|
||||
```
|
||||
|
||||
@ -452,12 +443,11 @@ for d in ds.list_docs(keywords="rag", offset=0, limit=12):
|
||||
## Delete documents
|
||||
|
||||
```python
|
||||
Document.delete() -> bool
|
||||
DataSet.delete_documents(ids: List[str] = None)
|
||||
```
|
||||
### Returns
|
||||
|
||||
bool
|
||||
description: delete success or not
|
||||
no return
|
||||
|
||||
### Examples
|
||||
|
||||
@ -465,119 +455,87 @@ description: delete success or not
|
||||
from ragflow import RAGFlow
|
||||
|
||||
rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
|
||||
ds = rag.create_dataset(name="kb_1")
|
||||
|
||||
filename1 = "~/ragflow.txt"
|
||||
rag.create_document(ds, name=filename1 , blob=open(filename1 , "rb").read())
|
||||
|
||||
filename2 = "~/infinity.txt"
|
||||
rag.create_document(ds, name=filename2 , blob=open(filename2 , "rb").read())
|
||||
for d in ds.list_docs(keywords="rag", offset=0, limit=12):
|
||||
d.delete()
|
||||
ds = rag.list_datasets(name="kb_1")
|
||||
ds = ds[0]
|
||||
ds.delete_documents(ids=["id_1","id_2"])
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Parse document
|
||||
## Parse and stop parsing document
|
||||
|
||||
```python
|
||||
Document.async_parse() -> None
|
||||
RAGFLOW.async_parse_documents() -> None
|
||||
DataSet.async_parse_documents(document_ids:List[str]) -> None
|
||||
DataSet.async_cancel_parse_documents(document_ids:List[str])-> None
|
||||
```
|
||||
|
||||
### Parameters
|
||||
|
||||
#### document_ids:`List[str]`
|
||||
The ids of the documents to be parsed
|
||||
????????????????????????????????????????????????????
|
||||
|
||||
### Returns
|
||||
|
||||
no return
|
||||
????????????????????????????????????????????????????
|
||||
|
||||
### Examples
|
||||
|
||||
```python
|
||||
#document parse and cancel
|
||||
rag = RAGFlow(API_KEY, HOST_ADDRESS)
|
||||
ds = rag.create_dataset(name="dataset_name")
|
||||
name3 = 'ai.pdf'
|
||||
path = 'test_data/ai.pdf'
|
||||
rag.create_document(ds, name=name3, blob=open(path, "rb").read())
|
||||
doc = rag.get_document(name="ai.pdf")
|
||||
doc.async_parse()
|
||||
print("Async parsing initiated")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Cancel document parsing
|
||||
|
||||
```python
|
||||
rag.async_cancel_parse_documents(ids)
|
||||
RAGFLOW.async_cancel_parse_documents()-> None
|
||||
```
|
||||
|
||||
### Parameters
|
||||
|
||||
#### ids, `list[]`
|
||||
|
||||
### Returns
|
||||
|
||||
?????????????????????????????????????????????????
|
||||
|
||||
### Examples
|
||||
|
||||
```python
|
||||
#documents parse and cancel
|
||||
rag = RAGFlow(API_KEY, HOST_ADDRESS)
|
||||
ds = rag.create_dataset(name="God5")
|
||||
documents = [
|
||||
{'name': 'test1.txt', 'path': 'test_data/test1.txt'},
|
||||
{'name': 'test2.txt', 'path': 'test_data/test2.txt'},
|
||||
{'name': 'test3.txt', 'path': 'test_data/test3.txt'}
|
||||
{'name': 'test1.txt', 'blob': open('./test_data/test1.txt',"rb").read()},
|
||||
{'name': 'test2.txt', 'blob': open('./test_data/test2.txt',"rb").read()},
|
||||
{'name': 'test3.txt', 'blob': open('./test_data/test3.txt',"rb").read()}
|
||||
]
|
||||
|
||||
# Create documents in bulk
|
||||
for doc_info in documents:
|
||||
with open(doc_info['path'], "rb") as file:
|
||||
created_doc = rag.create_document(ds, name=doc_info['name'], blob=file.read())
|
||||
docs = [rag.get_document(name=doc_info['name']) for doc_info in documents]
|
||||
ids = [doc.id for doc in docs]
|
||||
|
||||
rag.async_parse_documents(ids)
|
||||
ds.upload_documents(documents)
|
||||
documents=ds.list_documents(keywords="test")
|
||||
ids=[]
|
||||
for document in documents:
|
||||
ids.append(document.id)
|
||||
ds.async_parse_documents(ids)
|
||||
print("Async bulk parsing initiated")
|
||||
|
||||
for doc in docs:
|
||||
for progress, msg in doc.join(interval=5, timeout=10):
|
||||
print(f"{doc.name}: Progress: {progress}, Message: {msg}")
|
||||
|
||||
cancel_result = rag.async_cancel_parse_documents(ids)
|
||||
ds.async_cancel_parse_documents(ids)
|
||||
print("Async bulk parsing cancelled")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Join document
|
||||
|
||||
??????????????????
|
||||
|
||||
## List chunks
|
||||
```python
|
||||
Document.join(interval=15, timeout=3600) -> iteral[Tuple[float, str]]
|
||||
Document.list_chunks(keywords: str = None, offset: int = 0, limit: int = -1, id : str = None) -> List[Chunk]
|
||||
```
|
||||
|
||||
### Parameters
|
||||
|
||||
#### interval: `int`
|
||||
- `keywords`: `str`
|
||||
List chunks whose name has the given keywords
|
||||
default: `None`
|
||||
|
||||
Time interval in seconds for progress report. Defaults to `15`.
|
||||
- `offset`: `int`
|
||||
The beginning number of records for paging
|
||||
default: `1`
|
||||
|
||||
#### timeout: `int`
|
||||
|
||||
Timeout in seconds. Defaults to `3600`.
|
||||
- `limit`: `int`
|
||||
Records number to return
|
||||
default: `30`
|
||||
|
||||
- `id`: `str`
|
||||
The ID of the chunk to be retrieved
|
||||
default: `None`
|
||||
### Returns
|
||||
List[chunk]
|
||||
|
||||
iteral[Tuple[float, str]]
|
||||
### Examples
|
||||
```python
|
||||
from ragflow import RAGFlow
|
||||
|
||||
rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
|
||||
ds = rag.list_datasets("123")
|
||||
ds = ds[0]
|
||||
ds.async_parse_documents(["wdfxb5t547d"])
|
||||
for c in doc.list_chunks(keywords="rag", offset=0, limit=12):
|
||||
print(c)
|
||||
```
|
||||
## Add chunk
|
||||
|
||||
```python
|
||||
@ -587,6 +545,9 @@ Document.add_chunk(content:str) -> Chunk
|
||||
### Parameters
|
||||
|
||||
#### content: `str`, *Required*
|
||||
Contains the main text or information of the chunk.
|
||||
#### important_keywords :`List[str]`
|
||||
list the key terms or phrases that are significant or central to the chunk's content.
|
||||
|
||||
### Returns
|
||||
|
||||
@ -598,7 +559,10 @@ chunk
|
||||
from ragflow import RAGFlow
|
||||
|
||||
rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
|
||||
doc = rag.get_document(id="wdfxb5t547d")
|
||||
ds = rag.list_datasets(id="123")
|
||||
ds = ds[0]
|
||||
doc = ds.list_documents(id="wdfxb5t547d")
|
||||
doc = doc[0]
|
||||
chunk = doc.add_chunk(content="xxxxxxx")
|
||||
```
|
||||
|
||||
@ -607,12 +571,15 @@ chunk = doc.add_chunk(content="xxxxxxx")
|
||||
## Delete chunk
|
||||
|
||||
```python
|
||||
Chunk.delete() -> bool
|
||||
Document.delete_chunks(chunk_ids: List[str])
|
||||
```
|
||||
### Parameters
|
||||
#### chunk_ids:`List[str]`
|
||||
The list of chunk_id
|
||||
|
||||
### Returns
|
||||
|
||||
bool
|
||||
no return
|
||||
|
||||
### Examples
|
||||
|
||||
@ -620,22 +587,34 @@ bool
|
||||
from ragflow import RAGFlow
|
||||
|
||||
rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
|
||||
doc = rag.get_document(id="wdfxb5t547d")
|
||||
ds = rag.list_datasets(id="123")
|
||||
ds = ds[0]
|
||||
doc = ds.list_documents(id="wdfxb5t547d")
|
||||
doc = doc[0]
|
||||
chunk = doc.add_chunk(content="xxxxxxx")
|
||||
chunk.delete()
|
||||
doc.delete_chunks(["id_1","id_2"])
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Save chunk contents
|
||||
## Update chunk
|
||||
|
||||
```python
|
||||
Chunk.save() -> bool
|
||||
Chunk.update(update_message: dict)
|
||||
```
|
||||
### Parameters
|
||||
- `content`: `str`
|
||||
Contains the main text or information of the chunk
|
||||
|
||||
- `important_keywords`: `List[str]`
|
||||
List the key terms or phrases that are significant or central to the chunk's content
|
||||
|
||||
- `available`: `int`
|
||||
Indicating the availability status, `0` means unavailable and `1` means available
|
||||
|
||||
### Returns
|
||||
|
||||
bool
|
||||
no return
|
||||
|
||||
### Examples
|
||||
|
||||
@ -643,10 +622,12 @@ bool
|
||||
from ragflow import RAGFlow
|
||||
|
||||
rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
|
||||
doc = rag.get_document(id="wdfxb5t547d")
|
||||
ds = rag.list_datasets(id="123")
|
||||
ds = ds[0]
|
||||
doc = ds.list_documents(id="wdfxb5t547d")
|
||||
doc = doc[0]
|
||||
chunk = doc.add_chunk(content="xxxxxxx")
|
||||
chunk.content = "sdfx"
|
||||
chunk.save()
|
||||
chunk.update({"content":"sdfx...})
|
||||
```
|
||||
|
||||
---
|
||||
@ -654,7 +635,7 @@ chunk.save()
|
||||
## Retrieval
|
||||
|
||||
```python
|
||||
RAGFlow.retrieval(question:str, datasets:List[Dataset], document=List[Document]=None, offset:int=0, limit:int=6, similarity_threshold:float=0.1, vector_similarity_weight:float=0.3, top_k:int=1024) -> List[Chunk]
|
||||
RAGFlow.retrieve(question:str="", datasets:List[str]=None, document=List[str]=None, offset:int=1, limit:int=30, similarity_threshold:float=0.2, vector_similarity_weight:float=0.3, top_k:int=1024,rerank_id:str=None,keyword:bool=False,higlight:bool=False) -> List[Chunk]
|
||||
```
|
||||
|
||||
### Parameters
|
||||
@ -691,6 +672,15 @@ The weight of vector cosine similarity, 1 - x is the term similarity weight. Def
|
||||
|
||||
Number of records engaged in vector cosine computaton. Defaults to `1024`.
|
||||
|
||||
#### rerank_id:`str`
|
||||
ID of the rerank model. Defaults to `None`.
|
||||
|
||||
#### keyword:`bool`
|
||||
Indicating whether keyword-based matching is enabled (True) or disabled (False).
|
||||
|
||||
#### highlight:`bool`
|
||||
|
||||
Specifying whether to enable highlighting of matched terms in the results (True) or not (False).
|
||||
### Returns
|
||||
|
||||
List[Chunk]
|
||||
@ -701,18 +691,17 @@ List[Chunk]
|
||||
from ragflow import RAGFlow
|
||||
|
||||
rag = RAGFlow(api_key="xxxxxx", base_url="http://xxx.xx.xx.xxx:9380")
|
||||
ds = rag.get_dataset(name="ragflow")
|
||||
ds = rag.list_datasets(name="ragflow")
|
||||
ds = ds[0]
|
||||
name = 'ragflow_test.txt'
|
||||
path = 'test_data/ragflow_test.txt'
|
||||
path = './test_data/ragflow_test.txt'
|
||||
rag.create_document(ds, name=name, blob=open(path, "rb").read())
|
||||
doc = rag.get_document(name=name)
|
||||
doc.async_parse()
|
||||
# Wait for parsing to complete
|
||||
for progress, msg in doc.join(interval=5, timeout=30):
|
||||
print(progress, msg)
|
||||
for c in rag.retrieval(question="What's ragflow?",
|
||||
datasets=[ds], documents=[doc],
|
||||
offset=0, limit=6, similarity_threshold=0.1,
|
||||
doc = ds.list_documents(name=name)
|
||||
doc = doc[0]
|
||||
ds.async_parse_documents([doc.id])
|
||||
for c in rag.retrieve(question="What's ragflow?",
|
||||
datasets=[ds.id], documents=[doc.id],
|
||||
offset=1, limit=30, similarity_threshold=0.2,
|
||||
vector_similarity_weight=0.3,
|
||||
top_k=1024
|
||||
):
|
||||
|
||||
@ -17,32 +17,11 @@ class Chunk(Base):
|
||||
res_dict.pop(k)
|
||||
super().__init__(rag, res_dict)
|
||||
|
||||
def delete(self) -> bool:
|
||||
"""
|
||||
Delete the chunk in the document.
|
||||
"""
|
||||
res = self.post('/doc/chunk/rm',
|
||||
{"document_id": self.document_id, 'chunk_ids': [self.id]})
|
||||
res = res.json()
|
||||
if res.get("retmsg") == "success":
|
||||
return True
|
||||
raise Exception(res["retmsg"])
|
||||
|
||||
def save(self) -> bool:
|
||||
"""
|
||||
Save the document details to the server.
|
||||
"""
|
||||
res = self.post('/doc/chunk/set',
|
||||
{"chunk_id": self.id,
|
||||
"knowledgebase_id": self.knowledgebase_id,
|
||||
"name": self.document_name,
|
||||
"content": self.content,
|
||||
"important_keywords": self.important_keywords,
|
||||
"document_id": self.document_id,
|
||||
"available": self.available,
|
||||
})
|
||||
def update(self,update_message:dict):
|
||||
res = self.put(f"/dataset/{self.knowledgebase_id}/document/{self.document_id}/chunk/{self.id}",update_message)
|
||||
res = res.json()
|
||||
if res.get("retmsg") == "success":
|
||||
return True
|
||||
raise Exception(res["retmsg"])
|
||||
if res.get("code") != 0 :
|
||||
raise Exception(res["message"])
|
||||
|
||||
|
||||
|
||||
@ -65,3 +65,14 @@ class DataSet(Base):
|
||||
if res.get("code") != 0:
|
||||
raise Exception(res["message"])
|
||||
|
||||
def async_parse_documents(self,document_ids):
|
||||
res = self.post(f"/dataset/{self.id}/chunk",{"document_ids":document_ids})
|
||||
res = res.json()
|
||||
if res.get("code") != 0:
|
||||
raise Exception(res.get("message"))
|
||||
|
||||
def async_cancel_parse_documents(self,document_ids):
|
||||
res = self.rm(f"/dataset/{self.id}/chunk",{"document_ids":document_ids})
|
||||
res = res.json()
|
||||
if res.get("code") != 0:
|
||||
raise Exception(res.get("message"))
|
||||
|
||||
@ -1,7 +1,10 @@
|
||||
import time
|
||||
|
||||
from PIL.ImageFile import raise_oserror
|
||||
|
||||
from .base import Base
|
||||
from .chunk import Chunk
|
||||
from typing import List
|
||||
|
||||
|
||||
class Document(Base):
|
||||
@ -29,160 +32,28 @@ class Document(Base):
|
||||
res_dict.pop(k)
|
||||
super().__init__(rag, res_dict)
|
||||
|
||||
def update(self,update_message:dict) -> bool:
|
||||
"""
|
||||
Save the document details to the server.
|
||||
"""
|
||||
res = self.post(f'/dataset/{self.knowledgebase_id}/info/{self.id}',update_message)
|
||||
def list_chunks(self,offset=0, limit=30, keywords="", id:str=None):
|
||||
data={"document_id": self.id,"keywords": keywords,"offset":offset,"limit":limit,"id":id}
|
||||
res = self.get(f'/dataset/{self.knowledgebase_id}/document/{self.id}/chunk', data)
|
||||
res = res.json()
|
||||
if res.get("code") != 0:
|
||||
raise Exception(res["message"])
|
||||
if res.get("code") == 0:
|
||||
chunks=[]
|
||||
for data in res["data"].get("chunks"):
|
||||
chunk = Chunk(self.rag,data)
|
||||
chunks.append(chunk)
|
||||
return chunks
|
||||
raise Exception(res.get("message"))
|
||||
|
||||
def delete(self) -> bool:
|
||||
"""
|
||||
Delete the document from the server.
|
||||
"""
|
||||
res = self.rm('/doc/delete',
|
||||
{"document_id": self.id})
|
||||
res = res.json()
|
||||
if res.get("retmsg") == "success":
|
||||
return True
|
||||
raise Exception(res["retmsg"])
|
||||
|
||||
def download(self) -> bytes:
|
||||
"""
|
||||
Download the document content from the server using the Flask API.
|
||||
|
||||
:return: The downloaded document content in bytes.
|
||||
"""
|
||||
# Construct the URL for the API request using the document ID and knowledge base ID
|
||||
res = self.get(f"/dataset/{self.knowledgebase_id}/document/{self.id}")
|
||||
|
||||
# Check the response status code to ensure the request was successful
|
||||
if res.status_code == 200:
|
||||
# Return the document content as bytes
|
||||
return res.content
|
||||
else:
|
||||
# Handle the error and raise an exception
|
||||
raise Exception(
|
||||
f"Failed to download document. Server responded with: {res.status_code}, {res.text}"
|
||||
)
|
||||
|
||||
def async_parse(self):
|
||||
"""
|
||||
Initiate document parsing asynchronously without waiting for completion.
|
||||
"""
|
||||
try:
|
||||
# Construct request data including document ID and run status (assuming 1 means to run)
|
||||
data = {"document_ids": [self.id], "run": 1}
|
||||
|
||||
# Send a POST request to the specified parsing status endpoint to start parsing
|
||||
res = self.post(f'/doc/run', data)
|
||||
|
||||
# Check the server response status code
|
||||
if res.status_code != 200:
|
||||
raise Exception(f"Failed to start async parsing: {res.text}")
|
||||
|
||||
print("Async parsing started successfully.")
|
||||
|
||||
except Exception as e:
|
||||
# Catch and handle exceptions
|
||||
print(f"Error occurred during async parsing: {str(e)}")
|
||||
raise
|
||||
|
||||
import time
|
||||
|
||||
def join(self, interval=5, timeout=3600):
|
||||
"""
|
||||
Wait for the asynchronous parsing to complete and yield parsing progress periodically.
|
||||
|
||||
:param interval: The time interval (in seconds) for progress reports.
|
||||
:param timeout: The timeout (in seconds) for the parsing operation.
|
||||
:return: An iterator yielding parsing progress and messages.
|
||||
"""
|
||||
start_time = time.time()
|
||||
while time.time() - start_time < timeout:
|
||||
# Check the parsing status
|
||||
res = self.get(f'/doc/{self.id}/status', {"document_ids": [self.id]})
|
||||
res_data = res.json()
|
||||
data = res_data.get("data", [])
|
||||
|
||||
# Retrieve progress and status message
|
||||
progress = data.get("progress", 0)
|
||||
progress_msg = data.get("status", "")
|
||||
|
||||
yield progress, progress_msg # Yield progress and message
|
||||
|
||||
if progress == 100: # Parsing completed
|
||||
break
|
||||
|
||||
time.sleep(interval)
|
||||
|
||||
def cancel(self):
|
||||
"""
|
||||
Cancel the parsing task for the document.
|
||||
"""
|
||||
try:
|
||||
# Construct request data, including document ID and action to cancel (assuming 2 means cancel)
|
||||
data = {"document_ids": [self.id], "run": 2}
|
||||
|
||||
# Send a POST request to the specified parsing status endpoint to cancel parsing
|
||||
res = self.post(f'/doc/run', data)
|
||||
|
||||
# Check the server response status code
|
||||
if res.status_code != 200:
|
||||
print("Failed to cancel parsing. Server response:", res.text)
|
||||
else:
|
||||
print("Parsing cancelled successfully.")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error occurred during async parsing cancellation: {str(e)}")
|
||||
raise
|
||||
|
||||
def list_chunks(self, page=1, offset=0, limit=12,size=30, keywords="", available_int=None):
|
||||
"""
|
||||
List all chunks associated with this document by calling the external API.
|
||||
|
||||
Args:
|
||||
page (int): The page number to retrieve (default 1).
|
||||
size (int): The number of chunks per page (default 30).
|
||||
keywords (str): Keywords for searching specific chunks (default "").
|
||||
available_int (int): Filter for available chunks (optional).
|
||||
|
||||
Returns:
|
||||
list: A list of chunks returned from the API.
|
||||
"""
|
||||
data = {
|
||||
"document_id": self.id,
|
||||
"page": page,
|
||||
"size": size,
|
||||
"keywords": keywords,
|
||||
"offset":offset,
|
||||
"limit":limit
|
||||
}
|
||||
|
||||
if available_int is not None:
|
||||
data["available_int"] = available_int
|
||||
|
||||
res = self.post(f'/doc/chunk/list', data)
|
||||
if res.status_code == 200:
|
||||
res_data = res.json()
|
||||
if res_data.get("retmsg") == "success":
|
||||
chunks=[]
|
||||
for chunk_data in res_data["data"].get("chunks", []):
|
||||
chunk=Chunk(self.rag,chunk_data)
|
||||
chunks.append(chunk)
|
||||
return chunks
|
||||
else:
|
||||
raise Exception(f"Error fetching chunks: {res_data.get('retmsg')}")
|
||||
else:
|
||||
raise Exception(f"API request failed with status code {res.status_code}")
|
||||
|
||||
def add_chunk(self, content: str):
|
||||
res = self.post('/doc/chunk/create', {"document_id": self.id, "content":content})
|
||||
if res.status_code == 200:
|
||||
res_data = res.json().get("data")
|
||||
chunk_data = res_data.get("chunk")
|
||||
return Chunk(self.rag,chunk_data)
|
||||
else:
|
||||
raise Exception(f"Failed to add chunk: {res.status_code} {res.text}")
|
||||
res = self.post(f'/dataset/{self.knowledgebase_id}/document/{self.id}/chunk', {"content":content})
|
||||
res = res.json()
|
||||
if res.get("code") == 0:
|
||||
return Chunk(self.rag,res["data"].get("chunk"))
|
||||
raise Exception(res.get("message"))
|
||||
|
||||
def delete_chunks(self,ids:List[str]):
|
||||
res = self.rm(f"dataset/{self.knowledgebase_id}/document/{self.id}/chunk",{"ids":ids})
|
||||
res = res.json()
|
||||
if res.get("code")!=0:
|
||||
raise Exception(res.get("message"))
|
||||
@ -15,8 +15,8 @@ class Session(Base):
|
||||
for message in self.messages:
|
||||
if "reference" in message:
|
||||
message.pop("reference")
|
||||
res = self.post(f"/chat/{self.chat_id}/session/{self.id}/completion",
|
||||
{"question": question, "stream": True}, stream=stream)
|
||||
res = self.post(f"/chat/{self.chat_id}/completion",
|
||||
{"question": question, "stream": True,"session_id":self.id}, stream=stream)
|
||||
for line in res.iter_lines():
|
||||
line = line.decode("utf-8")
|
||||
if line.startswith("{"):
|
||||
@ -82,3 +82,4 @@ class Chunk(Base):
|
||||
self.term_similarity = None
|
||||
self.positions = None
|
||||
super().__init__(rag, res_dict)
|
||||
|
||||
|
||||
@ -158,105 +158,30 @@ class RAGFlow:
|
||||
raise Exception(res["message"])
|
||||
|
||||
|
||||
|
||||
def async_parse_documents(self, doc_ids):
|
||||
"""
|
||||
Asynchronously start parsing multiple documents without waiting for completion.
|
||||
|
||||
:param doc_ids: A list containing multiple document IDs.
|
||||
"""
|
||||
try:
|
||||
if not doc_ids or not isinstance(doc_ids, list):
|
||||
raise ValueError("doc_ids must be a non-empty list of document IDs")
|
||||
|
||||
data = {"document_ids": doc_ids, "run": 1}
|
||||
|
||||
res = self.post(f'/doc/run', data)
|
||||
|
||||
if res.status_code != 200:
|
||||
raise Exception(f"Failed to start async parsing for documents: {res.text}")
|
||||
|
||||
print(f"Async parsing started successfully for documents: {doc_ids}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error occurred during async parsing for documents: {str(e)}")
|
||||
raise
|
||||
|
||||
def async_cancel_parse_documents(self, doc_ids):
|
||||
"""
|
||||
Cancel the asynchronous parsing of multiple documents.
|
||||
|
||||
:param doc_ids: A list containing multiple document IDs.
|
||||
"""
|
||||
try:
|
||||
if not doc_ids or not isinstance(doc_ids, list):
|
||||
raise ValueError("doc_ids must be a non-empty list of document IDs")
|
||||
data = {"document_ids": doc_ids, "run": 2}
|
||||
res = self.post(f'/doc/run', data)
|
||||
|
||||
if res.status_code != 200:
|
||||
raise Exception(f"Failed to cancel async parsing for documents: {res.text}")
|
||||
|
||||
print(f"Async parsing canceled successfully for documents: {doc_ids}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error occurred during canceling parsing for documents: {str(e)}")
|
||||
raise
|
||||
|
||||
def retrieval(self,
|
||||
question,
|
||||
datasets=None,
|
||||
documents=None,
|
||||
offset=0,
|
||||
limit=6,
|
||||
similarity_threshold=0.1,
|
||||
vector_similarity_weight=0.3,
|
||||
top_k=1024):
|
||||
"""
|
||||
Perform document retrieval based on the given parameters.
|
||||
|
||||
:param question: The query question.
|
||||
:param datasets: A list of datasets (optional, as documents may be provided directly).
|
||||
:param documents: A list of documents (if specific documents are provided).
|
||||
:param offset: Offset for the retrieval results.
|
||||
:param limit: Maximum number of retrieval results.
|
||||
:param similarity_threshold: Similarity threshold.
|
||||
:param vector_similarity_weight: Weight of vector similarity.
|
||||
:param top_k: Number of top most similar documents to consider (for pre-filtering or ranking).
|
||||
|
||||
Note: This is a hypothetical implementation and may need adjustments based on the actual backend service API.
|
||||
"""
|
||||
try:
|
||||
data = {
|
||||
"question": question,
|
||||
"datasets": datasets if datasets is not None else [],
|
||||
"documents": [doc.id if hasattr(doc, 'id') else doc for doc in
|
||||
documents] if documents is not None else [],
|
||||
def retrieve(self, question="",datasets=None,documents=None, offset=1, limit=30, similarity_threshold=0.2,vector_similarity_weight=0.3,top_k=1024,rerank_id:str=None,keyword:bool=False,):
|
||||
data_params = {
|
||||
"offset": offset,
|
||||
"limit": limit,
|
||||
"similarity_threshold": similarity_threshold,
|
||||
"vector_similarity_weight": vector_similarity_weight,
|
||||
"top_k": top_k,
|
||||
"knowledgebase_id": datasets,
|
||||
"rerank_id":rerank_id,
|
||||
"keyword":keyword
|
||||
}
|
||||
data_json ={
|
||||
"question": question,
|
||||
"datasets": datasets,
|
||||
"documents": documents
|
||||
}
|
||||
|
||||
# Send a POST request to the backend service (using requests library as an example, actual implementation may vary)
|
||||
res = self.post(f'/doc/retrieval_test', data)
|
||||
|
||||
# Check the response status code
|
||||
if res.status_code == 200:
|
||||
res_data = res.json()
|
||||
if res_data.get("retmsg") == "success":
|
||||
chunks = []
|
||||
for chunk_data in res_data["data"].get("chunks", []):
|
||||
chunk = Chunk(self, chunk_data)
|
||||
chunks.append(chunk)
|
||||
return chunks
|
||||
else:
|
||||
raise Exception(f"Error fetching chunks: {res_data.get('retmsg')}")
|
||||
else:
|
||||
raise Exception(f"API request failed with status code {res.status_code}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"An error occurred during retrieval: {e}")
|
||||
raise
|
||||
res = self.get(f'/retrieval', data_params,data_json)
|
||||
res = res.json()
|
||||
if res.get("code") ==0:
|
||||
chunks=[]
|
||||
for chunk_data in res["data"].get("chunks"):
|
||||
chunk=Chunk(self,chunk_data)
|
||||
chunks.append(chunk)
|
||||
return chunks
|
||||
raise Exception(res.get("message"))
|
||||
|
||||
@ -63,17 +63,13 @@ class TestDocument(TestSdk):
|
||||
# Check if the retrieved document is of type Document
|
||||
if isinstance(doc, Document):
|
||||
# Download the document content and save it to a file
|
||||
try:
|
||||
with open("ragflow.txt", "wb+") as file:
|
||||
file.write(doc.download())
|
||||
# Print the document object for debugging
|
||||
print(doc)
|
||||
with open("./ragflow.txt", "wb+") as file:
|
||||
file.write(doc.download())
|
||||
# Print the document object for debugging
|
||||
print(doc)
|
||||
|
||||
# Assert that the download was successful
|
||||
assert True, "Document downloaded successfully."
|
||||
except Exception as e:
|
||||
# If an error occurs, raise an assertion error
|
||||
assert False, f"Failed to download document, error: {str(e)}"
|
||||
# Assert that the download was successful
|
||||
assert True, f"Failed to download document, error: {doc}"
|
||||
else:
|
||||
# If the document retrieval fails, assert failure
|
||||
assert False, f"Failed to get document, error: {doc}"
|
||||
@ -100,7 +96,7 @@ class TestDocument(TestSdk):
|
||||
blob2 = b"Sample document content for ingestion test222."
|
||||
list_1 = [{"name":name1,"blob":blob1},{"name":name2,"blob":blob2}]
|
||||
ds.upload_documents(list_1)
|
||||
for d in ds.list_docs(keywords="test", offset=0, limit=12):
|
||||
for d in ds.list_documents(keywords="test", offset=0, limit=12):
|
||||
assert isinstance(d, Document), "Failed to upload documents"
|
||||
|
||||
def test_delete_documents_in_dataset_with_success(self):
|
||||
@ -123,16 +119,11 @@ class TestDocument(TestSdk):
|
||||
blob1 = b"Sample document content for ingestion test333."
|
||||
name2 = "Test Document444.txt"
|
||||
blob2 = b"Sample document content for ingestion test444."
|
||||
name3 = 'test.txt'
|
||||
path = 'test_data/test.txt'
|
||||
rag.create_document(ds, name=name3, blob=open(path, "rb").read())
|
||||
rag.create_document(ds, name=name1, blob=blob1)
|
||||
rag.create_document(ds, name=name2, blob=blob2)
|
||||
for d in ds.list_docs(keywords="document", offset=0, limit=12):
|
||||
ds.upload_documents([{"name":name1,"blob":blob1},{"name":name2,"blob":blob2}])
|
||||
for d in ds.list_documents(keywords="document", offset=0, limit=12):
|
||||
assert isinstance(d, Document)
|
||||
d.delete()
|
||||
print(d)
|
||||
remaining_docs = ds.list_docs(keywords="rag", offset=0, limit=12)
|
||||
ds.delete_documents([d.id])
|
||||
remaining_docs = ds.list_documents(keywords="rag", offset=0, limit=12)
|
||||
assert len(remaining_docs) == 0, "Documents were not properly deleted."
|
||||
|
||||
def test_parse_and_cancel_document(self):
|
||||
@ -144,16 +135,15 @@ class TestDocument(TestSdk):
|
||||
|
||||
# Define the document name and path
|
||||
name3 = 'westworld.pdf'
|
||||
path = 'test_data/westworld.pdf'
|
||||
path = './test_data/westworld.pdf'
|
||||
|
||||
# Create a document in the dataset using the file path
|
||||
rag.create_document(ds, name=name3, blob=open(path, "rb").read())
|
||||
ds.upload_documents({"name":name3, "blob":open(path, "rb").read()})
|
||||
|
||||
# Retrieve the document by name
|
||||
doc = rag.get_document(name="westworld.pdf")
|
||||
|
||||
# Initiate asynchronous parsing
|
||||
doc.async_parse()
|
||||
doc = rag.list_documents(name="westworld.pdf")
|
||||
doc = doc[0]
|
||||
ds.async_parse_documents(document_ids=[])
|
||||
|
||||
# Print message to confirm asynchronous parsing has been initiated
|
||||
print("Async parsing initiated")
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user