Added pinecone, moved import stmts

src/beyondllm/retrievers/hybridRetriever.py

@@ -113,15 +113,6 @@ def initialize_from_data(self):
             )
         return vector_index, keyword_index
 
-    # def load_index(self):
-    #     vector_index = VectorStoreIndex(
-    #         self.data, embed_model=self.embed_model
-    #     )
-    #     keyword_index = SimpleKeywordTableIndex(
-    #         self.data, service_context=ServiceContext.from_defaults(llm=None,embed_model=None)
-    #     )
-    #     return vector_index, keyword_index
-
     def as_retriever(self):
         vector_index, keyword_index = self.load_index()
 

src/beyondllm/vectordb/__init__.py

@@ -1 +1,2 @@
-from .chroma import ChromaVectorDb
+from .chroma import ChromaVectorDb
+from .pinecone import PineconeVectorDb

src/beyondllm/vectordb/chroma.py

@@ -3,16 +3,6 @@
 import warnings
 warnings.filterwarnings("ignore")
 import subprocess,sys
-try:
-    from llama_index.vector_stores.chroma import ChromaVectorStore
-except ImportError:
-    user_agree = input("The feature you're trying to use requires an additional library(s):llama_index.vector_stores.chroma. Would you like to install it now? [y/N]: ")
-    if user_agree.lower() == 'y':
-        subprocess.check_call([sys.executable, "-m", "pip", "install", "llama_index.vector_stores.chroma"])
-        from llama_index.vector_stores.chroma import ChromaVectorStore
-    else:
-        raise ImportError("The required 'llama_index.vector_stores.chroma' is not installed.")
-import chromadb
 
 @dataclass
 class ChromaVectorDb:
@@ -24,6 +14,17 @@ class ChromaVectorDb:
     persist_directory: str = ""
 
     def __post_init__(self):
+        try:
+            from llama_index.vector_stores.chroma import ChromaVectorStore
+        except ImportError:
+            user_agree = input("The feature you're trying to use requires an additional library(s):llama_index.vector_stores.chroma. Would you like to install it now? [y/N]: ")
+            if user_agree.lower() == 'y':
+                subprocess.check_call([sys.executable, "-m", "pip", "install", "llama_index.vector_stores.chroma"])
+                from llama_index.vector_stores.chroma import ChromaVectorStore
+            else:
+                raise ImportError("The required 'llama_index.vector_stores.chroma' is not installed.")
+        import chromadb
+
         if self.persist_directory=="" or self.persist_directory==None:  
             self.chroma_client = chromadb.EphemeralClient()
         else:

src/beyondllm/vectordb/pinecone.py

@@ -0,0 +1,114 @@
+from beyondllm.vectordb.base import VectorDb, VectorDbConfig
+from dataclasses import dataclass, field
+import warnings
+warnings.filterwarnings("ignore")
+import os, subprocess, sys
+
+@dataclass
+class PineconeVectorDb:
+    """
+    from beyondllm.vectordb import PineconeVectorDb
+    # to load pre-existing index
+    vectordb = PineconeVectorDb(
+                    api_key="<your api key>",
+                    index_name="quickstart", 
+                )
+    # or 
+    # create new index
+    vectordb = PineconeVectorDb(create=True,
+                    api_key="<your api key>", index_name="quickstart", 
+                    embedding_dim=1536, metric="cosine",
+                    spec="serverless", cloud="aws", region="us-east-1"
+                )
+    """
+
+    api_key: str
+    index_name: str
+    create: bool = False
+    embedding_dim: int = None
+    metric: str = None
+    spec: str = "serverless"
+    cloud: str = None
+    region: str = None
+    pod_type: str = None  # Only required for pod-type Pinecone indexes
+    replicas: int = None  # Only required for pod-type Pinecone indexes
+
+    def __post_init__(self):
+        try:
+            from llama_index.vector_stores.pinecone import PineconeVectorStore
+            from pinecone import Pinecone, ServerlessSpec
+        except ImportError:
+            user_agree = input(
+                "The feature you're trying to use requires an additional library(s):llama_index.vector_stores.pinecone, pinecone-client. Would you like to install it now? [y/N]: "
+            )
+            if user_agree.lower() == "y":
+                subprocess.check_call([sys.executable, "-m", "pip", "install", "llama_index.vector_stores.pinecone"])
+                subprocess.check_call([sys.executable, "-m", "pip", "install", "pinecone-client"])
+                from llama_index.vector_stores.pinecone import PineconeVectorStore
+                from pinecone import Pinecone, ServerlessSpec
+            else:
+                raise ImportError("The required 'llama_index.vector_stores.pinecone and pinecone-client' are not installed.")
+
+        pinecone_client = Pinecone(api_key=self.api_key)
+
+        if self.create==False:
+            self.pinecone_index = pinecone_client.Index(self.index_name)
+        else:
+            if self.spec is "pod-based" or None:
+                from pinecone import PodSpec
+
+                pinecone_client.create_index(
+                    name=self.index_name,
+                    dimension=self.embedding_dim,
+                    metric=self.metric,
+                    spec=PodSpec(environment=self.environment, pod_type=self.pod_type, replicas=self.replicas),
+                )
+            else:
+                pinecone_client.create_index(
+                    name=self.index_name,
+                    dimension=self.embedding_dim,
+                    metric=self.metric,
+                    spec=ServerlessSpec(cloud=self.cloud, region=self.region),
+                )
+
+            self.pinecone_index = pinecone_client.Index(self.index_name)
+
+        self.load()
+
+    def load(self):
+        try:
+            from llama_index.vector_stores.pinecone import PineconeVectorStore
+        except:
+            raise ImportError(
+                "PineconeVectorStore library is not installed. Please install it with ``pip install llama_index.vector_stores.pinecone``."
+            )
+
+        try:
+            vector_store = PineconeVectorStore(pinecone_index=self.pinecone_index)
+            self.client = vector_store
+        except Exception as e:
+            raise Exception(f"Failed to load the Pinecone Vectorstore: {e}")
+
+        return self.client
+
+    def add(self, *args, **kwargs):
+        client = self.client
+        return client.add(*args, **kwargs)
+
+    def stores_text(self, *args, **kwargs):
+        client = self.client
+        return client.stores_text(*args, **kwargs)
+
+    def is_embedding_query(self, *args, **kwargs):
+        client = self.client
+        return client.is_embedding_query(*args, **kwargs)
+
+    def query(self, *args, **kwargs):
+        client = self.client
+        return client.query(*args, **kwargs)
+
+    @staticmethod
+    def load_from_kwargs(self, kwargs):
+        embed_config = VectorDbConfig(**kwargs)
+        self.config = embed_config
+        self.load()