A fix for Jupyter environment variable issue (#135)

- fixes the Jupyter environment variable issues mentioned in issue #134 
- fixes format/lint issues in some unrelated files (from make
format/lint)


![image](https://user-images.githubusercontent.com/347398/201599322-090af858-362d-4d69-bf59-208aea65419a.png)

langchain/chains/mapreduce.py

@@ -59,9 +59,7 @@ class MapReduceChain(Chain, BaseModel):
 
     def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
         # Split the larger text into smaller chunks.
-        docs = self.text_splitter.split_text(
+        docs = self.text_splitter.split_text(inputs[self.input_key],)
-            inputs[self.input_key],
-        )
         # Now that we have the chunks, we send them to the LLM and track results.
         #  This is the "map" part.
         summaries = []

langchain/chains/natbot/crawler.py

@@ -28,13 +28,7 @@ class Crawler:
                 "Could not import playwright python package. "
                 "Please it install it with `pip install playwright`."
             )
-        self.browser = (
+        self.browser = sync_playwright().start().chromium.launch(headless=False,)
-            sync_playwright()
-            .start()
-            .chromium.launch(
-                headless=False,
-            )
-        )
 
         self.page = self.browser.new_page()
         self.page.set_viewport_size({"width": 1280, "height": 1080})

langchain/chains/react/prompt.py

@@ -109,8 +109,4 @@ Action 3: Finish[yes]""",
 ]
 SUFFIX = """\n\nQuestion: {input}"""
 
-PROMPT = Prompt.from_examples(
+PROMPT = Prompt.from_examples(EXAMPLES, SUFFIX, ["input"],)
-    EXAMPLES,
-    SUFFIX,
-    ["input"],
-)

langchain/chains/self_ask_with_search/prompt.py

@@ -38,7 +38,4 @@ Intermediate Answer: New Zealand.
 So the final answer is: No
 
 Question: {input}"""
-PROMPT = Prompt(
+PROMPT = Prompt(input_variables=["input"], template=_DEFAULT_TEMPLATE,)
-    input_variables=["input"],
-    template=_DEFAULT_TEMPLATE,
-)

langchain/chains/sql_database/prompt.py

@@ -15,6 +15,5 @@ Only use the following tables:
 
 Question: {input}"""
 PROMPT = Prompt(
-    input_variables=["input", "table_info", "dialect"],
+    input_variables=["input", "table_info", "dialect"], template=_DEFAULT_TEMPLATE,
-    template=_DEFAULT_TEMPLATE,
 )

langchain/embeddings/cohere.py

@@ -1,10 +1,10 @@
 """Wrapper around Cohere embedding models."""
-import os
 from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Extra, root_validator
 
 from langchain.embeddings.base import Embeddings
+from langchain.llms.utils import get_from_dict_or_env
 
 
 class CohereEmbeddings(BaseModel, Embeddings):
@@ -25,7 +25,7 @@ class CohereEmbeddings(BaseModel, Embeddings):
     model: str = "medium"
     """Model name to use."""
 
-    cohere_api_key: Optional[str] = os.environ.get("COHERE_API_KEY")
+    cohere_api_key: Optional[str] = None
 
     class Config:
         """Configuration for this pydantic object."""
@@ -35,7 +35,9 @@ class CohereEmbeddings(BaseModel, Embeddings):
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
-        cohere_api_key = values.get("cohere_api_key")
+        cohere_api_key = get_from_dict_or_env(
+            values, "cohere_api_key", "COHERE_API_KEY"
+        )
 
         if cohere_api_key is None or cohere_api_key == "":
             raise ValueError(

langchain/embeddings/openai.py

@@ -1,10 +1,10 @@
 """Wrapper around OpenAI embedding models."""
-import os
 from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Extra, root_validator
 
 from langchain.embeddings.base import Embeddings
+from langchain.llms.utils import get_from_dict_or_env
 
 
 class OpenAIEmbeddings(BaseModel, Embeddings):
@@ -25,7 +25,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
     model_name: str = "babbage"
     """Model name to use."""
 
-    openai_api_key: Optional[str] = os.environ.get("OPENAI_API_KEY")
+    openai_api_key: Optional[str] = None
 
     class Config:
         """Configuration for this pydantic object."""
@@ -35,7 +35,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
-        openai_api_key = values.get("openai_api_key")
+        openai_api_key = get_from_dict_or_env(
+            values, "openai_api_key", "OPENAI_API_KEY"
+        )
 
         if openai_api_key is None or openai_api_key == "":
             raise ValueError(

langchain/llms/ai21.py

@@ -1,11 +1,11 @@
 """Wrapper around AI21 APIs."""
-import os
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
 from pydantic import BaseModel, Extra, root_validator
 
 from langchain.llms.base import LLM
+from langchain.llms.utils import get_from_dict_or_env
 
 
 class AI21PenaltyData(BaseModel):
@@ -62,7 +62,7 @@ class AI21(BaseModel, LLM):
     logitBias: Optional[Dict[str, float]] = None
     """Adjust the probability of specific tokens being generated."""
 
-    ai21_api_key: Optional[str] = os.environ.get("AI21_API_KEY")
+    ai21_api_key: Optional[str] = None
 
     class Config:
         """Configuration for this pydantic object."""
@@ -72,8 +72,7 @@ class AI21(BaseModel, LLM):
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key exists in environment."""
-        ai21_api_key = values.get("ai21_api_key")
+        ai21_api_key = get_from_dict_or_env(values, "ai21_api_key", "AI21_API_KEY")
-
         if ai21_api_key is None or ai21_api_key == "":
             raise ValueError(
                 "Did not find AI21 API key, please add an environment variable"
@@ -122,11 +121,7 @@ class AI21(BaseModel, LLM):
         response = requests.post(
             url=f"https://api.ai21.com/studio/v1/{self.model}/complete",
             headers={"Authorization": f"Bearer {self.ai21_api_key}"},
-            json={
+            json={"prompt": prompt, "stopSequences": stop, **self._default_params,},
-                "prompt": prompt,
-                "stopSequences": stop,
-                **self._default_params,
-            },
         )
         if response.status_code != 200:
             optional_detail = response.json().get("error")

langchain/llms/cohere.py

@@ -1,11 +1,10 @@
 """Wrapper around Cohere APIs."""
-import os
 from typing import Any, Dict, List, Mapping, Optional
 
 from pydantic import BaseModel, Extra, root_validator
 
 from langchain.llms.base import LLM
-from langchain.llms.utils import enforce_stop_tokens
+from langchain.llms.utils import enforce_stop_tokens, get_from_dict_or_env
 
 
 class Cohere(LLM, BaseModel):
@@ -44,7 +43,7 @@ class Cohere(LLM, BaseModel):
     presence_penalty: int = 0
     """Penalizes repeated tokens."""
 
-    cohere_api_key: Optional[str] = os.environ.get("COHERE_API_KEY")
+    cohere_api_key: Optional[str] = None
 
     class Config:
         """Configuration for this pydantic object."""
@@ -54,7 +53,9 @@ class Cohere(LLM, BaseModel):
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
-        cohere_api_key = values.get("cohere_api_key")
+        cohere_api_key = get_from_dict_or_env(
+            values, "cohere_api_key", "COHERE_API_KEY"
+        )
 
         if cohere_api_key is None or cohere_api_key == "":
             raise ValueError(

langchain/llms/huggingface_hub.py

@@ -1,11 +1,10 @@
 """Wrapper around HuggingFace APIs."""
-import os
 from typing import Any, Dict, List, Mapping, Optional
 
 from pydantic import BaseModel, Extra, root_validator
 
 from langchain.llms.base import LLM
-from langchain.llms.utils import enforce_stop_tokens
+from langchain.llms.utils import enforce_stop_tokens, get_from_dict_or_env
 
 DEFAULT_REPO_ID = "gpt2"
 VALID_TASKS = ("text2text-generation", "text-generation")
@@ -18,7 +17,7 @@ class HuggingFaceHub(LLM, BaseModel):
     environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass
     it as a named parameter to the constructor.
 
-    Only supports task `text-generation` for now.
+    Only supports `text-generation` and `text2text-generation` for now.
 
     Example:
         .. code-block:: python
@@ -35,7 +34,7 @@ class HuggingFaceHub(LLM, BaseModel):
     model_kwargs: Optional[dict] = None
     """Key word arguments to pass to the model."""
 
-    huggingfacehub_api_token: Optional[str] = os.environ.get("HUGGINGFACEHUB_API_TOKEN")
+    huggingfacehub_api_token: Optional[str] = None
 
     class Config:
         """Configuration for this pydantic object."""
@@ -45,7 +44,9 @@ class HuggingFaceHub(LLM, BaseModel):
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
-        huggingfacehub_api_token = values.get("huggingfacehub_api_token")
+        huggingfacehub_api_token = get_from_dict_or_env(
+            values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
+        )
         if huggingfacehub_api_token is None or huggingfacehub_api_token == "":
             raise ValueError(
                 "Did not find HuggingFace API token, please add an environment variable"

langchain/llms/nlpcloud.py

@@ -1,10 +1,10 @@
 """Wrapper around NLPCloud APIs."""
-import os
 from typing import Any, Dict, List, Mapping, Optional
 
 from pydantic import BaseModel, Extra, root_validator
 
 from langchain.llms.base import LLM
+from langchain.llms.utils import get_from_dict_or_env
 
 
 class NLPCloud(LLM, BaseModel):
@@ -54,7 +54,7 @@ class NLPCloud(LLM, BaseModel):
     num_return_sequences: int = 1
     """How many completions to generate for each prompt."""
 
-    nlpcloud_api_key: Optional[str] = os.environ.get("NLPCLOUD_API_KEY")
+    nlpcloud_api_key: Optional[str] = None
 
     class Config:
         """Configuration for this pydantic object."""
@@ -64,7 +64,9 @@ class NLPCloud(LLM, BaseModel):
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
-        nlpcloud_api_key = values.get("nlpcloud_api_key")
+        nlpcloud_api_key = get_from_dict_or_env(
+            values, "nlpcloud_api_key", "NLPCLOUD_API_KEY"
+        )
 
         if nlpcloud_api_key is None or nlpcloud_api_key == "":
             raise ValueError(

langchain/llms/openai.py

@@ -1,10 +1,10 @@
 """Wrapper around OpenAI APIs."""
-import os
 from typing import Any, Dict, List, Mapping, Optional
 
 from pydantic import BaseModel, Extra, root_validator
 
 from langchain.llms.base import LLM
+from langchain.llms.utils import get_from_dict_or_env
 
 
 class OpenAI(LLM, BaseModel):
@@ -38,7 +38,7 @@ class OpenAI(LLM, BaseModel):
     best_of: int = 1
     """Generates best_of completions server-side and returns the "best"."""
 
-    openai_api_key: Optional[str] = os.environ.get("OPENAI_API_KEY")
+    openai_api_key: Optional[str] = None
 
     class Config:
         """Configuration for this pydantic object."""
@@ -48,7 +48,9 @@ class OpenAI(LLM, BaseModel):
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
-        openai_api_key = values.get("openai_api_key")
+        openai_api_key = get_from_dict_or_env(
+            values, "openai_api_key", "OPENAI_API_KEY"
+        )
 
         if openai_api_key is None or openai_api_key == "":
             raise ValueError(

langchain/llms/utils.py

@@ -1,8 +1,16 @@
 """Common utility functions for working with LLM APIs."""
+import os
 import re
-from typing import List
+from typing import Any, Dict, List
 
 
 def enforce_stop_tokens(text: str, stop: List[str]) -> str:
     """Cut off the text as soon as any stop words occur."""
     return re.split("|".join(stop), text)[0]
+
+
+def get_from_dict_or_env(data: Dict[str, Any], key: str, env_key: str) -> Any:
+    """Get a value from a dictionary or an environment variable."""
+    if key in data:
+        return data[key]
+    return os.environ.get(env_key, None)

langchain/vectorstores/elastic_vector_search.py

@@ -45,10 +45,7 @@ class ElasticVectorSearch(VectorStore):
     """
 
     def __init__(
-        self,
+        self, elasticsearch_url: str, index_name: str, embedding_function: Callable,
-        elasticsearch_url: str,
-        index_name: str,
-        embedding_function: Callable,
     ):
         """Initialize with necessary components."""
         try:

tests/integration_tests/llms/test_manifest.py

@@ -6,9 +6,7 @@ def test_manifest_wrapper() -> None:
     """Test manifest wrapper."""
     from manifest import Manifest
 
-    manifest = Manifest(
+    manifest = Manifest(client_name="openai",)
-        client_name="openai",
-    )
     llm = ManifestWrapper(client=manifest, llm_kwargs={"temperature": 0})
     output = llm("The capital of New York is:")
     assert output == "Albany"