community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)

Moved the following modules to new package langchain-community in a backwards compatible fashion:

```
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
```

Moved the following to core
```
mv langchain/langchain/utils/json_schema.py core/langchain_core/utils
mv langchain/langchain/utils/html.py core/langchain_core/utils
mv langchain/langchain/utils/strings.py core/langchain_core/utils
cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py
rm langchain/langchain/utils/env.py
```

See .scripts/community_split/script_integrations.sh for all changes

libs/community/langchain_community/embeddings/embaas.py

@@ -0,0 +1,156 @@
+from typing import Any, Dict, List, Mapping, Optional
+
+import requests
+from langchain_core.embeddings import Embeddings
+from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
+from langchain_core.utils import get_from_dict_or_env
+from requests.adapters import HTTPAdapter, Retry
+from typing_extensions import NotRequired, TypedDict
+
+# Currently supported maximum batch size for embedding requests
+MAX_BATCH_SIZE = 256
+EMBAAS_API_URL = "https://api.embaas.io/v1/embeddings/"
+
+
+class EmbaasEmbeddingsPayload(TypedDict):
+    """Payload for the Embaas embeddings API."""
+
+    model: str
+    texts: List[str]
+    instruction: NotRequired[str]
+
+
+class EmbaasEmbeddings(BaseModel, Embeddings):
+    """Embaas's embedding service.
+
+    To use, you should have the
+    environment variable ``EMBAAS_API_KEY`` set with your API key, or pass
+    it as a named parameter to the constructor.
+
+    Example:
+        .. code-block:: python
+
+            # Initialise with default model and instruction
+            from langchain_community.embeddings import EmbaasEmbeddings
+            emb = EmbaasEmbeddings()
+
+            # Initialise with custom model and instruction
+            from langchain_community.embeddings import EmbaasEmbeddings
+            emb_model = "instructor-large"
+            emb_inst = "Represent the Wikipedia document for retrieval"
+            emb = EmbaasEmbeddings(
+                model=emb_model,
+                instruction=emb_inst
+            )
+    """
+
+    model: str = "e5-large-v2"
+    """The model used for embeddings."""
+    instruction: Optional[str] = None
+    """Instruction used for domain-specific embeddings."""
+    api_url: str = EMBAAS_API_URL
+    """The URL for the embaas embeddings API."""
+    embaas_api_key: Optional[str] = None
+    """max number of retries for requests"""
+    max_retries: Optional[int] = 3
+    """request timeout in seconds"""
+    timeout: Optional[int] = 30
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        embaas_api_key = get_from_dict_or_env(
+            values, "embaas_api_key", "EMBAAS_API_KEY"
+        )
+        values["embaas_api_key"] = embaas_api_key
+        return values
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying params."""
+        return {"model": self.model, "instruction": self.instruction}
+
+    def _generate_payload(self, texts: List[str]) -> EmbaasEmbeddingsPayload:
+        """Generates payload for the API request."""
+        payload = EmbaasEmbeddingsPayload(texts=texts, model=self.model)
+        if self.instruction:
+            payload["instruction"] = self.instruction
+        return payload
+
+    def _handle_request(self, payload: EmbaasEmbeddingsPayload) -> List[List[float]]:
+        """Sends a request to the Embaas API and handles the response."""
+        headers = {
+            "Authorization": f"Bearer {self.embaas_api_key}",
+            "Content-Type": "application/json",
+        }
+
+        session = requests.Session()
+        retries = Retry(
+            total=self.max_retries,
+            backoff_factor=0.5,
+            allowed_methods=["POST"],
+            raise_on_status=True,
+        )
+
+        session.mount("http://", HTTPAdapter(max_retries=retries))
+        session.mount("https://", HTTPAdapter(max_retries=retries))
+        response = session.post(
+            self.api_url,
+            headers=headers,
+            json=payload,
+            timeout=self.timeout,
+        )
+
+        parsed_response = response.json()
+        embeddings = [item["embedding"] for item in parsed_response["data"]]
+
+        return embeddings
+
+    def _generate_embeddings(self, texts: List[str]) -> List[List[float]]:
+        """Generate embeddings using the Embaas API."""
+        payload = self._generate_payload(texts)
+        try:
+            return self._handle_request(payload)
+        except requests.exceptions.RequestException as e:
+            if e.response is None or not e.response.text:
+                raise ValueError(f"Error raised by embaas embeddings API: {e}")
+
+            parsed_response = e.response.json()
+            if "message" in parsed_response:
+                raise ValueError(
+                    "Validation Error raised by embaas embeddings API:"
+                    f"{parsed_response['message']}"
+                )
+            raise
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Get embeddings for a list of texts.
+
+        Args:
+            texts: The list of texts to get embeddings for.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        batches = [
+            texts[i : i + MAX_BATCH_SIZE] for i in range(0, len(texts), MAX_BATCH_SIZE)
+        ]
+        embeddings = [self._generate_embeddings(batch) for batch in batches]
+        # flatten the list of lists into a single list
+        return [embedding for batch in embeddings for embedding in batch]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Get embeddings for a single text.
+
+        Args:
+            text: The text to get embeddings for.
+
+        Returns:
+            List of embeddings.
+        """
+        return self.embed_documents([text])[0]