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/utilities/outline.py

@@ -0,0 +1,95 @@
+"""Util that calls Outline."""
+import logging
+from typing import Any, Dict, List, Optional
+
+import requests
+from langchain_core.documents import Document
+from langchain_core.pydantic_v1 import BaseModel, root_validator
+from langchain_core.utils import get_from_dict_or_env
+
+logger = logging.getLogger(__name__)
+
+OUTLINE_MAX_QUERY_LENGTH = 300
+
+
+class OutlineAPIWrapper(BaseModel):
+    """Wrapper around OutlineAPI.
+
+    This wrapper will use the Outline API to query the documents of your instance.
+    By default it will return the document content of the top-k results.
+    It limits the document content by doc_content_chars_max.
+    """
+
+    top_k_results: int = 3
+    load_all_available_meta: bool = False
+    doc_content_chars_max: int = 4000
+    outline_instance_url: Optional[str] = None
+    outline_api_key: Optional[str] = None
+    outline_search_endpoint: str = "/api/documents.search"
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that instance url and api key exists in environment."""
+        outline_instance_url = get_from_dict_or_env(
+            values, "outline_instance_url", "OUTLINE_INSTANCE_URL"
+        )
+        values["outline_instance_url"] = outline_instance_url
+
+        outline_api_key = get_from_dict_or_env(
+            values, "outline_api_key", "OUTLINE_API_KEY"
+        )
+        values["outline_api_key"] = outline_api_key
+
+        return values
+
+    def _result_to_document(self, outline_res: Any) -> Document:
+        main_meta = {
+            "title": outline_res["document"]["title"],
+            "source": self.outline_instance_url + outline_res["document"]["url"],
+        }
+        add_meta = (
+            {
+                "id": outline_res["document"]["id"],
+                "ranking": outline_res["ranking"],
+                "collection_id": outline_res["document"]["collectionId"],
+                "parent_document_id": outline_res["document"]["parentDocumentId"],
+                "revision": outline_res["document"]["revision"],
+                "created_by": outline_res["document"]["createdBy"]["name"],
+            }
+            if self.load_all_available_meta
+            else {}
+        )
+        doc = Document(
+            page_content=outline_res["document"]["text"][: self.doc_content_chars_max],
+            metadata={
+                **main_meta,
+                **add_meta,
+            },
+        )
+        return doc
+
+    def _outline_api_query(self, query: str) -> List:
+        raw_result = requests.post(
+            f"{self.outline_instance_url}{self.outline_search_endpoint}",
+            data={"query": query, "limit": self.top_k_results},
+            headers={"Authorization": f"Bearer {self.outline_api_key}"},
+        )
+
+        if not raw_result.ok:
+            raise ValueError("Outline API returned an error: ", raw_result.text)
+
+        return raw_result.json()["data"]
+
+    def run(self, query: str) -> List[Document]:
+        """
+        Run Outline search and get the document content plus the meta information.
+
+        Returns: a list of documents.
+
+        """
+        results = self._outline_api_query(query[:OUTLINE_MAX_QUERY_LENGTH])
+        docs = []
+        for result in results[: self.top_k_results]:
+            if doc := self._result_to_document(result):
+                docs.append(doc)
+        return docs