Use qdrant instead of faiss as vectorstore

put back faiss
vemonet · Jan 11, 2024 · af9d5af · af9d5af
1 parent c5817fc
commit af9d5af
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Showing 3 changed files with 39 additions and 41 deletions.
diff --git a/config/chat-vectorstore-qa.yml b/config/chat-vectorstore-qa.yml
@@ -17,8 +17,7 @@ llm:
     Helpful answer:
 
 vector:
-  vector_path: "qdrant"
-  # vector_path: ./vectorstore/qdrant # Path to the vectorstore to do QA retrieval
+  vector_path: ./vectorstore/db_faiss # Path to the vectorstore to do QA retrieval
   vector_download: null
   embeddings_path: ./embeddings/all-MiniLM-L6-v2 # Embeddings used to generate the vectors. To use from HF: sentence-transformers/all-MiniLM-L6-v2
   embeddings_download: https://public.ukp.informatik.tu-darmstadt.de/reimers/sentence-transformers/v0.2/all-MiniLM-L6-v2.zip

diff --git a/docker-compose.yml b/docker-compose.yml
@@ -42,22 +42,22 @@ services:
       - nginx
 
 
-  qdrant:
-    # https://hub.docker.com/r/qdrant/qdrant/tags
-    image: qdrant/qdrant:v1.5.1
-    restart: unless-stopped
-    volumes:
-      - ./data/vectorstore/qdrant:/qdrant/storage
-      # - ./scripts/qdrant_config.yml:/qdrant/config/production.yaml
-    environment:
-      - QDRANT_ALLOW_RECOVERY_MODE=true
-      # - VIRTUAL_HOST=qdrant.137.120.31.148.nip.io
-      # - LETSENCRYPT_HOST=qdrant.137.120.31.148.nip.io
-      # - VIRTUAL_PORT=6333
-    # ports:
-    #   - 6333:6333
-    # command:
-    #   - ./qdrant --config-path /qdrant/qdrant_config.yml
+  # qdrant:
+  #   # https://hub.docker.com/r/qdrant/qdrant/tags
+  #   image: qdrant/qdrant:v1.5.1
+  #   restart: unless-stopped
+  #   volumes:
+  #     - ./data/vectorstore/qdrant:/qdrant/storage
+  #     # - ./scripts/qdrant_config.yml:/qdrant/config/production.yaml
+  #   environment:
+  #     - QDRANT_ALLOW_RECOVERY_MODE=true
+  #     # - VIRTUAL_HOST=qdrant.137.120.31.148.nip.io
+  #     # - LETSENCRYPT_HOST=qdrant.137.120.31.148.nip.io
+  #     # - VIRTUAL_PORT=6333
+  #   # ports:
+  #   #   - 6333:6333
+  #   # command:
+  #   #   - ./qdrant --config-path /qdrant/qdrant_config.yml
 
 
 # Also required to deploy containers publicly

diff --git a/src/libre_chat/llm.py b/src/libre_chat/llm.py
@@ -27,7 +27,7 @@
 )
 from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_community.llms import LlamaCpp
-from langchain_community.vectorstores import Qdrant
+from langchain_community.vectorstores import FAISS
 
 from libre_chat.conf import ChatConf, default_conf
 from libre_chat.utils import BOLD, CYAN, END, log, parallel_download
@@ -163,18 +163,16 @@ def get_llm(self, config: Optional[Dict[str, Any]] = None) -> LlamaCpp:
     def setup_dbqa(self) -> None:
         """Setup the vectorstore for QA"""
         if self.has_vectorstore():
-            from qdrant_client import QdrantClient
-
             embeddings = HuggingFaceEmbeddings(
                 model_name=self.conf.vector.embeddings_path, model_kwargs={"device": self.device}
             )
             # FAISS should automatically use GPU?
-            # vectorstore = FAISS.load_local(self.get_vectorstore(), embeddings)
-            vectorstore = Qdrant(
-                QdrantClient(host=self.conf.vector.vector_path, prefer_grpc=True),
-                collection_name="libre_chat_rag",
-                embeddings=embeddings,
-            )
+            vectorstore = FAISS.load_local(self.get_vectorstore(), embeddings)
+            # vectorstore = Qdrant(
+            #     QdrantClient(host=self.conf.vector.vector_path, prefer_grpc=True),
+            #     collection_name="libre_chat_rag",
+            #     embeddings=embeddings,
+            # )
 
             search_args: Dict[str, Any] = {"k": self.conf.vector.return_sources_count}
             if self.conf.vector.score_threshold is not None:
@@ -189,7 +187,7 @@ def setup_dbqa(self) -> None:
                 chain_type_kwargs={"prompt": self.prompt},
             )
 
-    def build_vectorstore(self, documents_path: Optional[str] = None) -> Optional[Qdrant]:
+    def build_vectorstore(self, documents_path: Optional[str] = None) -> Optional[FAISS]:
         """Build vectorstore from documents."""
         # https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/vectorstores/qdrant.py
         time_start = datetime.now()
@@ -226,19 +224,20 @@ def build_vectorstore(self, documents_path: Optional[str] = None) -> Optional[Qd
             embeddings = HuggingFaceEmbeddings(
                 model_name=self.conf.vector.embeddings_path, model_kwargs={"device": self.device}
             )
-            os.makedirs(str(self.conf.vector.vector_path), exist_ok=True)
-            vectorstore = Qdrant.from_documents(
-                splitted_texts,
-                embeddings,
-                # path=self.conf.vector.vector_path,
-                host=self.conf.vector.vector_path,
-                collection_name="libre_chat_rag",
-                prefer_grpc=True,
-                # force_recreate=True,
-            )
-            # vectorstore = FAISS.from_documents(splitted_texts, embeddings)
-            # if self.vector_path:
-            #     vectorstore.save_local(self.vector_path)
+            # TODO: use Qdrant vectorstore
+            # os.makedirs(str(self.conf.vector.vector_path), exist_ok=True)
+            # vectorstore = Qdrant.from_documents(
+            #     splitted_texts,
+            #     embeddings,
+            #     # path=self.conf.vector.vector_path,
+            #     host=self.conf.vector.vector_path,
+            #     collection_name="libre_chat_rag",
+            #     prefer_grpc=True,
+            #     # force_recreate=True,
+            # )
+            vectorstore = FAISS.from_documents(splitted_texts, embeddings)
+            if self.vector_path:
+                vectorstore.save_local(self.vector_path)
             log.info(f"✅ Vectorstore built in {datetime.now() - time_start}")
             return vectorstore
         return None