add raptor (#899)

### What problem does this PR solve?

#882 

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

api/apps/system_app.py

@@ -60,7 +60,8 @@ def status():
     st = timer()
     try:
         qinfo = REDIS_CONN.health(SVR_QUEUE_NAME)
-        res["redis"] = {"status": "green", "elapsed": "{:.1f}".format((timer() - st)*1000.), "pending": qinfo["pending"]}
+        res["redis"] = {"status": "green", "elapsed": "{:.1f}".format((timer() - st)*1000.),
+                        "pending": qinfo.get("pending", 0)}
     except Exception as e:
         res["redis"] = {"status": "red", "elapsed": "{:.1f}".format((timer() - st)*1000.), "error": str(e)}
 

api/db/services/document_service.py

@@ -18,8 +18,10 @@ from datetime import datetime
 from elasticsearch_dsl import Q
 from peewee import fn
 
+from api.db.db_utils import bulk_insert_into_db
 from api.settings import stat_logger
-from api.utils import current_timestamp, get_format_time
+from api.utils import current_timestamp, get_format_time, get_uuid
+from rag.settings import SVR_QUEUE_NAME
 from rag.utils.es_conn import ELASTICSEARCH
 from rag.utils.minio_conn import MINIO
 from rag.nlp import search
@@ -30,6 +32,7 @@ from api.db.db_models import Document
 from api.db.services.common_service import CommonService
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db import StatusEnum
+from rag.utils.redis_conn import REDIS_CONN
 
 
 class DocumentService(CommonService):
@@ -110,7 +113,7 @@ class DocumentService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_unfinished_docs(cls):
-        fields = [cls.model.id, cls.model.process_begin_at]
+        fields = [cls.model.id, cls.model.process_begin_at, cls.model.parser_config, cls.model.progress_msg]
         docs = cls.model.select(*fields) \
             .where(
                 cls.model.status == StatusEnum.VALID.value,
@@ -260,7 +263,12 @@ class DocumentService(CommonService):
                     prg = -1
                     status = TaskStatus.FAIL.value
                 elif finished:
-                    status = TaskStatus.DONE.value
+                    if d["parser_config"].get("raptor") and d["progress_msg"].lower().find(" raptor")<0:
+                        queue_raptor_tasks(d)
+                        prg *= 0.98
+                        msg.append("------ RAPTOR -------")
+                    else:
+                        status = TaskStatus.DONE.value
 
                 msg = "\n".join(msg)
                 info = {
@@ -282,3 +290,19 @@ class DocumentService(CommonService):
         return len(cls.model.select(cls.model.id).where(
             cls.model.kb_id == kb_id).dicts())
 
+
+def queue_raptor_tasks(doc):
+    def new_task():
+        nonlocal doc
+        return {
+            "id": get_uuid(),
+            "doc_id": doc["id"],
+            "from_page": 0,
+            "to_page": -1,
+            "progress_msg": "Start to do RAPTOR (Recursive Abstractive Processing For Tree-Organized Retrieval)."
+        }
+
+    task = new_task()
+    bulk_insert_into_db(Task, [task], True)
+    task["type"] = "raptor"
+    assert REDIS_CONN.queue_product(SVR_QUEUE_NAME, message=task), "Can't access Redis. Please check the Redis' status."

api/db/services/llm_service.py

@@ -155,6 +155,10 @@ class LLMBundle(object):
             tenant_id, llm_type, llm_name, lang=lang)
         assert self.mdl, "Can't find mole for {}/{}/{}".format(
             tenant_id, llm_type, llm_name)
+        self.max_length = 512
+        for lm in LLMService.query(llm_name=llm_name):
+            self.max_length = lm.max_tokens
+            break
 
     def encode(self, texts: list, batch_size=32):
         emd, used_tokens = self.mdl.encode(texts, batch_size)

api/db/services/task_service.py

@@ -53,6 +53,7 @@ class TaskService(CommonService):
             Knowledgebase.embd_id,
             Tenant.img2txt_id,
             Tenant.asr_id,
+            Tenant.llm_id,
             cls.model.update_time]
         docs = cls.model.select(*fields) \
             .join(Document, on=(cls.model.doc_id == Document.id)) \
@@ -159,4 +160,4 @@ def queue_tasks(doc, bucket, name):
     DocumentService.begin2parse(doc["id"])
 
     for t in tsks:
-        assert REDIS_CONN.queue_product(SVR_QUEUE_NAME, message=t), "Can't access Redis. Please check the Redis' status."
+        assert REDIS_CONN.queue_product(SVR_QUEUE_NAME, message=t), "Can't access Redis. Please check the Redis' status."

rag/llm/chat_model.py

@@ -57,8 +57,7 @@ class Base(ABC):
                 stream=True,
                 **gen_conf)
             for resp in response:
-                if len(resp.choices) == 0:continue
-                if not resp.choices[0].delta.content:continue
+                if not resp.choices or not resp.choices[0].delta.content:continue
                 ans += resp.choices[0].delta.content
                 total_tokens += 1
                 if resp.choices[0].finish_reason == "length":
@@ -379,7 +378,7 @@ class VolcEngineChat(Base):
                 ans += resp.choices[0].message.content
                 yield ans
                 if resp.choices[0].finish_reason == "stop":
-                    return resp.usage.total_tokens
+                    yield resp.usage.total_tokens
 
         except Exception as e:
             yield ans + "\n**ERROR**: " + str(e)

rag/raptor.py

@@ -0,0 +1,114 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import re
+import traceback
+from concurrent.futures import ThreadPoolExecutor, ALL_COMPLETED, wait
+from threading import Lock
+from typing import Tuple
+import umap
+import numpy as np
+from sklearn.mixture import GaussianMixture
+
+from rag.utils import num_tokens_from_string, truncate
+
+
+class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
+    def __init__(self, max_cluster, llm_model, embd_model, prompt, max_token=256, threshold=0.1):
+        self._max_cluster = max_cluster
+        self._llm_model = llm_model
+        self._embd_model = embd_model
+        self._threshold = threshold
+        self._prompt = prompt
+        self._max_token = max_token
+
+    def _get_optimal_clusters(self, embeddings: np.ndarray, random_state:int):
+        max_clusters = min(self._max_cluster, len(embeddings))
+        n_clusters = np.arange(1, max_clusters)
+        bics = []
+        for n in n_clusters:
+            gm = GaussianMixture(n_components=n, random_state=random_state)
+            gm.fit(embeddings)
+            bics.append(gm.bic(embeddings))
+        optimal_clusters = n_clusters[np.argmin(bics)]
+        return optimal_clusters
+
+    def __call__(self, chunks: Tuple[str, np.ndarray], random_state, callback=None):
+        layers = [(0, len(chunks))]
+        start, end = 0, len(chunks)
+        if len(chunks) <= 1: return
+
+        def summarize(ck_idx, lock):
+            nonlocal chunks
+            try:
+                texts = [chunks[i][0] for i in ck_idx]
+                len_per_chunk = int((self._llm_model.max_length - self._max_token)/len(texts))
+                cluster_content = "\n".join([truncate(t, max(1, len_per_chunk)) for t in texts])
+                cnt = self._llm_model.chat("You're a helpful assistant.",
+                                             [{"role": "user", "content": self._prompt.format(cluster_content=cluster_content)}],
+                                             {"temperature": 0.3, "max_tokens": self._max_token}
+                                             )
+                cnt = re.sub("(······\n由于长度的原因，回答被截断了，要继续吗？|For the content length reason, it stopped, continue?)", "", cnt)
+                print("SUM:", cnt)
+                embds, _ = self._embd_model.encode([cnt])
+                with lock:
+                    chunks.append((cnt, embds[0]))
+            except Exception as e:
+                print(e, flush=True)
+                traceback.print_stack(e)
+                return e
+
+        labels = []
+        while end - start > 1:
+            embeddings = [embd for _, embd in chunks[start: end]]
+            if len(embeddings) == 2:
+                summarize([start, start+1], Lock())
+                if callback:
+                    callback(msg="Cluster one layer: {} -> {}".format(end-start, len(chunks)-end))
+                labels.extend([0,0])
+                layers.append((end, len(chunks)))
+                start = end
+                end = len(chunks)
+                continue
+
+            n_neighbors = int((len(embeddings) - 1) ** 0.8)
+            reduced_embeddings = umap.UMAP(
+                n_neighbors=max(2, n_neighbors), n_components=min(12, len(embeddings)-2), metric="cosine"
+            ).fit_transform(embeddings)
+            n_clusters = self._get_optimal_clusters(reduced_embeddings, random_state)
+            if n_clusters == 1:
+                lbls = [0 for _ in range(len(reduced_embeddings))]
+            else:
+                gm = GaussianMixture(n_components=n_clusters, random_state=random_state)
+                gm.fit(reduced_embeddings)
+                probs = gm.predict_proba(reduced_embeddings)
+                lbls = [np.where(prob > self._threshold)[0] for prob in probs]
+            lock = Lock()
+            with ThreadPoolExecutor(max_workers=12) as executor:
+                threads = []
+                for c in range(n_clusters):
+                    ck_idx = [i+start for i in range(len(lbls)) if lbls[i] == c]
+                    threads.append(executor.submit(summarize, ck_idx, lock))
+                wait(threads, return_when=ALL_COMPLETED)
+                print([t.result() for t in threads])
+
+            assert len(chunks) - end == n_clusters, "{} vs. {}".format(len(chunks) - end, n_clusters)
+            labels.extend(lbls)
+            layers.append((end, len(chunks)))
+            if callback:
+                callback(msg="Cluster one layer: {} -> {}".format(end-start, len(chunks)-end))
+            start = end
+            end = len(chunks)
+

rag/svr/task_executor.py

@@ -26,20 +26,22 @@ import traceback
 from functools import partial
 
 from api.db.services.file2document_service import File2DocumentService
+from api.settings import retrievaler
+from rag.raptor import RecursiveAbstractiveProcessing4TreeOrganizedRetrieval as Raptor
 from rag.utils.minio_conn import MINIO
 from api.db.db_models import close_connection
 from rag.settings import database_logger, SVR_QUEUE_NAME
 from rag.settings import cron_logger, DOC_MAXIMUM_SIZE
 from multiprocessing import Pool
 import numpy as np
-from elasticsearch_dsl import Q
+from elasticsearch_dsl import Q, Search
 from multiprocessing.context import TimeoutError
 from api.db.services.task_service import TaskService
 from rag.utils.es_conn import ELASTICSEARCH
 from timeit import default_timer as timer
-from rag.utils import rmSpace, findMaxTm
+from rag.utils import rmSpace, findMaxTm, num_tokens_from_string
 
-from rag.nlp import search
+from rag.nlp import search, rag_tokenizer
 from io import BytesIO
 import pandas as pd
 
@@ -114,6 +116,8 @@ def collect():
     tasks = TaskService.get_tasks(msg["id"])
     assert tasks, "{} empty task!".format(msg["id"])
     tasks = pd.DataFrame(tasks)
+    if msg.get("type", "") == "raptor":
+        tasks["task_type"] = "raptor"
     return tasks
 
 
@@ -245,6 +249,47 @@ def embedding(docs, mdl, parser_config={}, callback=None):
     return tk_count
 
 
+def run_raptor(row, chat_mdl, embd_mdl, callback=None):
+    vts, _ = embd_mdl.encode(["ok"])
+    vctr_nm = "q_%d_vec"%len(vts[0])
+    chunks = []
+    for d in retrievaler.chunk_list(row["doc_id"], row["tenant_id"], fields=["content_with_weight", vctr_nm]):
+        chunks.append((d["content_with_weight"], np.array(d[vctr_nm])))
+
+    raptor = Raptor(
+        row["parser_config"]["raptor"].get("max_cluster", 64),
+        chat_mdl,
+        embd_mdl,
+        row["parser_config"]["raptor"]["prompt"],
+        row["parser_config"]["raptor"]["max_token"],
+        row["parser_config"]["raptor"]["threshold"]
+    )
+    original_length = len(chunks)
+    raptor(chunks, row["parser_config"]["raptor"]["random_seed"], callback)
+    doc = {
+        "doc_id": row["doc_id"],
+        "kb_id": [str(row["kb_id"])],
+        "docnm_kwd": row["name"],
+        "title_tks": rag_tokenizer.tokenize(row["name"])
+    }
+    res = []
+    tk_count = 0
+    for content, vctr in chunks[original_length:]:
+        d = copy.deepcopy(doc)
+        md5 = hashlib.md5()
+        md5.update((content + str(d["doc_id"])).encode("utf-8"))
+        d["_id"] = md5.hexdigest()
+        d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
+        d["create_timestamp_flt"] = datetime.datetime.now().timestamp()
+        d[vctr_nm] = vctr.tolist()
+        d["content_with_weight"] = content
+        d["content_ltks"] = rag_tokenizer.tokenize(content)
+        d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
+        res.append(d)
+        tk_count += num_tokens_from_string(content)
+    return res, tk_count
+
+
 def main():
     rows = collect()
     if len(rows) == 0:
@@ -259,35 +304,45 @@ def main():
             cron_logger.error(str(e))
             continue
 
-        st = timer()
-        cks = build(r)
-        cron_logger.info("Build chunks({}): {}".format(r["name"], timer() - st))
-        if cks is None:
-            continue
-        if not cks:
-            callback(1., "No chunk! Done!")
-            continue
-        # TODO: exception handler
-        ## set_progress(r["did"], -1, "ERROR: ")
-        callback(
-            msg="Finished slicing files(%d). Start to embedding the content." %
-                len(cks))
-        st = timer()
-        try:
-            tk_count = embedding(cks, embd_mdl, r["parser_config"], callback)
-        except Exception as e:
-            callback(-1, "Embedding error:{}".format(str(e)))
-            cron_logger.error(str(e))
-            tk_count = 0
-        cron_logger.info("Embedding elapsed({}): {:.2f}".format(r["name"], timer() - st))
+        if r.get("task_type", "") == "raptor":
+            try:
+                chat_mdl = LLMBundle(r["tenant_id"], LLMType.CHAT, llm_name=r["llm_id"], lang=r["language"])
+                cks, tk_count = run_raptor(r, chat_mdl, embd_mdl, callback)
+            except Exception as e:
+                callback(-1, msg=str(e))
+                cron_logger.error(str(e))
+                continue
+        else:
+            st = timer()
+            cks = build(r)
+            cron_logger.info("Build chunks({}): {}".format(r["name"], timer() - st))
+            if cks is None:
+                continue
+            if not cks:
+                callback(1., "No chunk! Done!")
+                continue
+            # TODO: exception handler
+            ## set_progress(r["did"], -1, "ERROR: ")
+            callback(
+                msg="Finished slicing files(%d). Start to embedding the content." %
+                    len(cks))
+            st = timer()
+            try:
+                tk_count = embedding(cks, embd_mdl, r["parser_config"], callback)
+            except Exception as e:
+                callback(-1, "Embedding error:{}".format(str(e)))
+                cron_logger.error(str(e))
+                tk_count = 0
+            cron_logger.info("Embedding elapsed({}): {:.2f}".format(r["name"], timer() - st))
+            callback(msg="Finished embedding({:.2f})! Start to build index!".format(timer() - st))
 
-        callback(msg="Finished embedding({:.2f})! Start to build index!".format(timer() - st))
         init_kb(r)
         chunk_count = len(set([c["_id"] for c in cks]))
         st = timer()
         es_r = ""
-        for b in range(0, len(cks), 32):
-            es_r = ELASTICSEARCH.bulk(cks[b:b + 32], search.index_name(r["tenant_id"]))
+        es_bulk_size = 16
+        for b in range(0, len(cks), es_bulk_size):
+            es_r = ELASTICSEARCH.bulk(cks[b:b + es_bulk_size], search.index_name(r["tenant_id"]))
             if b % 128 == 0:
                 callback(prog=0.8 + 0.1 * (b + 1) / len(cks), msg="")
 

rag/utils/redis_conn.py

@@ -97,15 +97,17 @@ class RedisDB:
         return False
 
     def queue_product(self, queue, message, exp=settings.SVR_QUEUE_RETENTION) -> bool:
-        try:
-            payload = {"message": json.dumps(message)}
-            pipeline = self.REDIS.pipeline()
-            pipeline.xadd(queue, payload)
-            pipeline.expire(queue, exp)
-            pipeline.execute()
-            return True
-        except Exception as e:
-            logging.warning("[EXCEPTION]producer" + str(queue) + "||" + str(e))
+        for _ in range(3):
+            try:
+                payload = {"message": json.dumps(message)}
+                pipeline = self.REDIS.pipeline()
+                pipeline.xadd(queue, payload)
+                pipeline.expire(queue, exp)
+                pipeline.execute()
+                return True
+            except Exception as e:
+                print(e)
+                logging.warning("[EXCEPTION]producer" + str(queue) + "||" + str(e))
         return False
 
     def queue_consumer(self, queue_name, group_name, consumer_name, msg_id=b">") -> Payload: