status stringclasses 1
value | repo_name stringclasses 31
values | repo_url stringclasses 31
values | issue_id int64 1 104k | title stringlengths 4 233 | body stringlengths 0 186k ⌀ | issue_url stringlengths 38 56 | pull_url stringlengths 37 54 | before_fix_sha stringlengths 40 40 | after_fix_sha stringlengths 40 40 | report_datetime timestamp[us, tz=UTC] | language stringclasses 5
values | commit_datetime timestamp[us, tz=UTC] | updated_file stringlengths 7 188 | chunk_content stringlengths 1 1.03M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,944 | Question Answering over Docs giving cryptic error upon query | After ingesting some markdown files using a slightly modified version of the question-answering over docs example, I ran the qa.py script as it was in the example
```
# qa.py
import faiss
from langchain import OpenAI, HuggingFaceHub, LLMChain
from langchain.chains import VectorDBQAWithSourcesChain
import pickle... | https://github.com/langchain-ai/langchain/issues/2944 | https://github.com/langchain-ai/langchain/pull/3026 | 3453b7457ca60227430d85e6f6f58a2aafae559d | 19c85aa9907765c0a2dbe7c46e9d5dd2d6df0f30 | 2023-04-15T15:38:36Z | python | 2023-04-18T03:28:01Z | langchain/chains/combine_documents/refine.py | """Combine by mapping first chain over all, then stuffing into final chain."""
inputs = self._construct_initial_inputs(docs, **kwargs)
res = self.initial_llm_chain.predict(**inputs)
refine_steps = [res]
for doc in docs[1:]:
base_inputs = self._construct_refine_inputs(doc, res... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,944 | Question Answering over Docs giving cryptic error upon query | After ingesting some markdown files using a slightly modified version of the question-answering over docs example, I ran the qa.py script as it was in the example
```
# qa.py
import faiss
from langchain import OpenAI, HuggingFaceHub, LLMChain
from langchain.chains import VectorDBQAWithSourcesChain
import pickle... | https://github.com/langchain-ai/langchain/issues/2944 | https://github.com/langchain-ai/langchain/pull/3026 | 3453b7457ca60227430d85e6f6f58a2aafae559d | 19c85aa9907765c0a2dbe7c46e9d5dd2d6df0f30 | 2023-04-15T15:38:36Z | python | 2023-04-18T03:28:01Z | langchain/chains/combine_documents/refine.py | if self.return_intermediate_steps:
extra_return_dict = {"intermediate_steps": refine_steps}
else:
extra_return_dict = {}
return res, extra_return_dict
def _construct_refine_inputs(self, doc: Document, res: str) -> Dict[str, Any]:
base_info = {"page_content": doc.page_... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,944 | Question Answering over Docs giving cryptic error upon query | After ingesting some markdown files using a slightly modified version of the question-answering over docs example, I ran the qa.py script as it was in the example
```
# qa.py
import faiss
from langchain import OpenAI, HuggingFaceHub, LLMChain
from langchain.chains import VectorDBQAWithSourcesChain
import pickle... | https://github.com/langchain-ai/langchain/issues/2944 | https://github.com/langchain-ai/langchain/pull/3026 | 3453b7457ca60227430d85e6f6f58a2aafae559d | 19c85aa9907765c0a2dbe7c46e9d5dd2d6df0f30 | 2023-04-15T15:38:36Z | python | 2023-04-18T03:28:01Z | langchain/chains/combine_documents/stuff.py | """Chain that combines documents by stuffing into context."""
from typing import Any, Dict, List, Optional, Tuple
from pydantic import Extra, Field, root_validator
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.chains.llm import LLMChain
from langchain.docstore.document imp... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,944 | Question Answering over Docs giving cryptic error upon query | After ingesting some markdown files using a slightly modified version of the question-answering over docs example, I ran the qa.py script as it was in the example
```
# qa.py
import faiss
from langchain import OpenAI, HuggingFaceHub, LLMChain
from langchain.chains import VectorDBQAWithSourcesChain
import pickle... | https://github.com/langchain-ai/langchain/issues/2944 | https://github.com/langchain-ai/langchain/pull/3026 | 3453b7457ca60227430d85e6f6f58a2aafae559d | 19c85aa9907765c0a2dbe7c46e9d5dd2d6df0f30 | 2023-04-15T15:38:36Z | python | 2023-04-18T03:28:01Z | langchain/chains/combine_documents/stuff.py | """Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator(pre=True)
def get_default_document_variable_name(cls, values: Dict) -> Dict:
"""Get default document variable name, if not provided."""
if "document_variable_name" no... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,944 | Question Answering over Docs giving cryptic error upon query | After ingesting some markdown files using a slightly modified version of the question-answering over docs example, I ran the qa.py script as it was in the example
```
# qa.py
import faiss
from langchain import OpenAI, HuggingFaceHub, LLMChain
from langchain.chains import VectorDBQAWithSourcesChain
import pickle... | https://github.com/langchain-ai/langchain/issues/2944 | https://github.com/langchain-ai/langchain/pull/3026 | 3453b7457ca60227430d85e6f6f58a2aafae559d | 19c85aa9907765c0a2dbe7c46e9d5dd2d6df0f30 | 2023-04-15T15:38:36Z | python | 2023-04-18T03:28:01Z | langchain/chains/combine_documents/stuff.py | doc_dicts = []
for doc in docs:
base_info = {"page_content": doc.page_content}
base_info.update(doc.metadata)
document_info = {
k: base_info[k] for k in self.document_prompt.input_variables
}
doc_dicts.append(document_info)
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,944 | Question Answering over Docs giving cryptic error upon query | After ingesting some markdown files using a slightly modified version of the question-answering over docs example, I ran the qa.py script as it was in the example
```
# qa.py
import faiss
from langchain import OpenAI, HuggingFaceHub, LLMChain
from langchain.chains import VectorDBQAWithSourcesChain
import pickle... | https://github.com/langchain-ai/langchain/issues/2944 | https://github.com/langchain-ai/langchain/pull/3026 | 3453b7457ca60227430d85e6f6f58a2aafae559d | 19c85aa9907765c0a2dbe7c46e9d5dd2d6df0f30 | 2023-04-15T15:38:36Z | python | 2023-04-18T03:28:01Z | langchain/chains/combine_documents/stuff.py | """Get the prompt length by formatting the prompt."""
inputs = self._get_inputs(docs, **kwargs)
prompt = self.llm_chain.prompt.format(**inputs)
return self.llm_chain.llm.get_num_tokens(prompt)
def combine_docs(self, docs: List[Document], **kwargs: Any) -> Tuple[str, dict]:
"""Stuff a... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,944 | Question Answering over Docs giving cryptic error upon query | After ingesting some markdown files using a slightly modified version of the question-answering over docs example, I ran the qa.py script as it was in the example
```
# qa.py
import faiss
from langchain import OpenAI, HuggingFaceHub, LLMChain
from langchain.chains import VectorDBQAWithSourcesChain
import pickle... | https://github.com/langchain-ai/langchain/issues/2944 | https://github.com/langchain-ai/langchain/pull/3026 | 3453b7457ca60227430d85e6f6f58a2aafae559d | 19c85aa9907765c0a2dbe7c46e9d5dd2d6df0f30 | 2023-04-15T15:38:36Z | python | 2023-04-18T03:28:01Z | tests/unit_tests/chains/test_combine_documents.py | """Test functionality related to combining documents."""
from typing import Any, List
import pytest
from langchain.chains.combine_documents.map_reduce import (
_collapse_docs,
_split_list_of_docs,
)
from langchain.docstore.document import Document
def _fake_docs_len_func(docs: List[Document]) -> int:
return... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,944 | Question Answering over Docs giving cryptic error upon query | After ingesting some markdown files using a slightly modified version of the question-answering over docs example, I ran the qa.py script as it was in the example
```
# qa.py
import faiss
from langchain import OpenAI, HuggingFaceHub, LLMChain
from langchain.chains import VectorDBQAWithSourcesChain
import pickle... | https://github.com/langchain-ai/langchain/issues/2944 | https://github.com/langchain-ai/langchain/pull/3026 | 3453b7457ca60227430d85e6f6f58a2aafae559d | 19c85aa9907765c0a2dbe7c46e9d5dd2d6df0f30 | 2023-04-15T15:38:36Z | python | 2023-04-18T03:28:01Z | tests/unit_tests/chains/test_combine_documents.py | """Test splitting works with just two docs."""
docs = [Document(page_content="foo"), Document(page_content="bar")]
doc_list = _split_list_of_docs(docs, _fake_docs_len_func, 100)
assert doc_list == [docs]
def test__split_list_works_correctly() -> None:
"""Test splitting works correctly."""
docs = [
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,944 | Question Answering over Docs giving cryptic error upon query | After ingesting some markdown files using a slightly modified version of the question-answering over docs example, I ran the qa.py script as it was in the example
```
# qa.py
import faiss
from langchain import OpenAI, HuggingFaceHub, LLMChain
from langchain.chains import VectorDBQAWithSourcesChain
import pickle... | https://github.com/langchain-ai/langchain/issues/2944 | https://github.com/langchain-ai/langchain/pull/3026 | 3453b7457ca60227430d85e6f6f58a2aafae559d | 19c85aa9907765c0a2dbe7c46e9d5dd2d6df0f30 | 2023-04-15T15:38:36Z | python | 2023-04-18T03:28:01Z | tests/unit_tests/chains/test_combine_documents.py | """Test collapse documents functionality when no metadata."""
docs = [
Document(page_content="foo"),
Document(page_content="bar"),
Document(page_content="baz"),
]
output = _collapse_docs(docs, _fake_combine_docs_func)
expected_output = Document(page_content="foobarbaz")
asser... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,944 | Question Answering over Docs giving cryptic error upon query | After ingesting some markdown files using a slightly modified version of the question-answering over docs example, I ran the qa.py script as it was in the example
```
# qa.py
import faiss
from langchain import OpenAI, HuggingFaceHub, LLMChain
from langchain.chains import VectorDBQAWithSourcesChain
import pickle... | https://github.com/langchain-ai/langchain/issues/2944 | https://github.com/langchain-ai/langchain/pull/3026 | 3453b7457ca60227430d85e6f6f58a2aafae559d | 19c85aa9907765c0a2dbe7c46e9d5dd2d6df0f30 | 2023-04-15T15:38:36Z | python | 2023-04-18T03:28:01Z | tests/unit_tests/chains/test_combine_documents.py | """Test collapse documents functionality when only one document present."""
docs = [Document(page_content="foo")]
output = _collapse_docs(docs, _fake_combine_docs_func)
assert output == docs[0]
docs = [Document(page_content="foo", metadata={"source": "a"})]
output = _collapse_docs(docs, _f... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,874 | Redundunt piece of code | In Agents -> loading.py on line 40 there is a redundant piece of code.
```
if config_type not in AGENT_TO_CLASS:
raise ValueError(f"Loading {config_type} agent not supported")
``` | https://github.com/langchain-ai/langchain/issues/2874 | https://github.com/langchain-ai/langchain/pull/2934 | b40f90ea042b20440cb7c1a9e70a6e4cd4a0089c | ae7ed31386c10cee1683419a4ab45562830bf8eb | 2023-04-14T05:28:42Z | python | 2023-04-18T04:05:48Z | langchain/agents/loading.py | """Functionality for loading agents."""
import json
from pathlib import Path
from typing import Any, Dict, List, Optional, Type, Union
import yaml
from langchain.agents.agent import BaseSingleActionAgent
from langchain.agents.agent_types import AgentType
from langchain.agents.chat.base import ChatAgent
from langchain.a... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,874 | Redundunt piece of code | In Agents -> loading.py on line 40 there is a redundant piece of code.
```
if config_type not in AGENT_TO_CLASS:
raise ValueError(f"Loading {config_type} agent not supported")
``` | https://github.com/langchain-ai/langchain/issues/2874 | https://github.com/langchain-ai/langchain/pull/2934 | b40f90ea042b20440cb7c1a9e70a6e4cd4a0089c | ae7ed31386c10cee1683419a4ab45562830bf8eb | 2023-04-14T05:28:42Z | python | 2023-04-18T04:05:48Z | langchain/agents/loading.py | config: dict, llm: BaseLLM, tools: List[Tool], **kwargs: Any
) -> BaseSingleActionAgent:
config_type = config.pop("_type")
if config_type not in AGENT_TO_CLASS:
raise ValueError(f"Loading {config_type} agent not supported")
if config_type not in AGENT_TO_CLASS:
raise ValueError(f"Loading {co... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,874 | Redundunt piece of code | In Agents -> loading.py on line 40 there is a redundant piece of code.
```
if config_type not in AGENT_TO_CLASS:
raise ValueError(f"Loading {config_type} agent not supported")
``` | https://github.com/langchain-ai/langchain/issues/2874 | https://github.com/langchain-ai/langchain/pull/2934 | b40f90ea042b20440cb7c1a9e70a6e4cd4a0089c | ae7ed31386c10cee1683419a4ab45562830bf8eb | 2023-04-14T05:28:42Z | python | 2023-04-18T04:05:48Z | langchain/agents/loading.py | ) -> BaseSingleActionAgent:
"""Load agent from Config Dict."""
if "_type" not in config:
raise ValueError("Must specify an agent Type in config")
load_from_tools = config.pop("load_from_llm_and_tools", False)
if load_from_tools:
if llm is None:
raise ValueError(
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,874 | Redundunt piece of code | In Agents -> loading.py on line 40 there is a redundant piece of code.
```
if config_type not in AGENT_TO_CLASS:
raise ValueError(f"Loading {config_type} agent not supported")
``` | https://github.com/langchain-ai/langchain/issues/2874 | https://github.com/langchain-ai/langchain/pull/2934 | b40f90ea042b20440cb7c1a9e70a6e4cd4a0089c | ae7ed31386c10cee1683419a4ab45562830bf8eb | 2023-04-14T05:28:42Z | python | 2023-04-18T04:05:48Z | langchain/agents/loading.py | """Unified method for loading a agent from LangChainHub or local fs."""
if hub_result := try_load_from_hub(
path, _load_agent_from_file, "agents", {"json", "yaml"}
):
return hub_result
else:
return _load_agent_from_file(path, **kwargs)
def _load_agent_from_file(
file: Union[str, ... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,057 | Error when parsing code from LLM response ValueError: Could not parse LLM output: | Sometimes the LLM response (generated code) tends to miss the ending ticks "```". Therefore causing the text parsing to fail due to `not enough values to unpack`.
Suggest to simply the `_, action, _' to just `action` then with index
Error message below
```
> Entering new AgentExecutor chain...
Traceback (mo... | https://github.com/langchain-ai/langchain/issues/3057 | https://github.com/langchain-ai/langchain/pull/3058 | db968284f8f3964630f119c95cca923f112ad47b | 2984ad39645c80411cee5e7f77a3c116b88d008e | 2023-04-18T04:13:20Z | python | 2023-04-18T04:42:13Z | langchain/agents/chat/output_parser.py | import json
from typing import Union
from langchain.agents.agent import AgentOutputParser
from langchain.schema import AgentAction, AgentFinish
FINAL_ANSWER_ACTION = "Final Answer:"
class ChatOutputParser(AgentOutputParser):
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
if FINAL_ANSWER_ACTI... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,157 | Missing Observation and Thought prefix in output | The console output when running a tool is missing the "Observation" and "Thought" prefixes.
I noticed this when using the SQL Toolkit, but other tools are likely affected.
Here is the current INCORRECT output format:
```
> Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""invoice_... | https://github.com/langchain-ai/langchain/issues/3157 | https://github.com/langchain-ai/langchain/pull/3158 | 126d7f11dd17a8ea71a4427951f10cefc862ba3a | 0b542661b46d42ee501c6681a4519f2c4e76de23 | 2023-04-19T15:15:26Z | python | 2023-04-19T16:00:10Z | langchain/tools/base.py | """Base implementation for tools or skills."""
from abc import ABC, abstractmethod
from inspect import signature
from typing import Any, Dict, Optional, Sequence, Tuple, Type, Union
from pydantic import BaseModel, Extra, Field, validate_arguments, validator
from langchain.callbacks import get_callback_manager
from lang... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,157 | Missing Observation and Thought prefix in output | The console output when running a tool is missing the "Observation" and "Thought" prefixes.
I noticed this when using the SQL Toolkit, but other tools are likely affected.
Here is the current INCORRECT output format:
```
> Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""invoice_... | https://github.com/langchain-ai/langchain/issues/3157 | https://github.com/langchain-ai/langchain/pull/3158 | 126d7f11dd17a8ea71a4427951f10cefc862ba3a | 0b542661b46d42ee501c6681a4519f2c4e76de23 | 2023-04-19T15:15:26Z | python | 2023-04-19T16:00:10Z | langchain/tools/base.py | if self.args_schema is not None:
return self.args_schema.schema()["properties"]
else:
inferred_model = validate_arguments(self._run).model
schema = inferred_model.schema()["properties"]
valid_keys = signature(self._run).parameters
return {k: schema[k... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,157 | Missing Observation and Thought prefix in output | The console output when running a tool is missing the "Observation" and "Thought" prefixes.
I noticed this when using the SQL Toolkit, but other tools are likely affected.
Here is the current INCORRECT output format:
```
> Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""invoice_... | https://github.com/langchain-ai/langchain/issues/3157 | https://github.com/langchain-ai/langchain/pull/3158 | 126d7f11dd17a8ea71a4427951f10cefc862ba3a | 0b542661b46d42ee501c6681a4519f2c4e76de23 | 2023-04-19T15:15:26Z | python | 2023-04-19T16:00:10Z | langchain/tools/base.py | """Use the tool."""
@abstractmethod
async def _arun(self, *args: Any, **kwargs: Any) -> str:
"""Use the tool asynchronously."""
def run(
self,
tool_input: Union[str, Dict],
verbose: Optional[bool] = None,
start_color: Optional[str] = "green",
color: Optional[s... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,157 | Missing Observation and Thought prefix in output | The console output when running a tool is missing the "Observation" and "Thought" prefixes.
I noticed this when using the SQL Toolkit, but other tools are likely affected.
Here is the current INCORRECT output format:
```
> Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""invoice_... | https://github.com/langchain-ai/langchain/issues/3157 | https://github.com/langchain-ai/langchain/pull/3158 | 126d7f11dd17a8ea71a4427951f10cefc862ba3a | 0b542661b46d42ee501c6681a4519f2c4e76de23 | 2023-04-19T15:15:26Z | python | 2023-04-19T16:00:10Z | langchain/tools/base.py | except (Exception, KeyboardInterrupt) as e:
self.callback_manager.on_tool_error(e, verbose=verbose_)
raise e
self.callback_manager.on_tool_end(
observation, verbose=verbose_, color=color, name=self.name, **kwargs
)
return observation
async def arun(
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,157 | Missing Observation and Thought prefix in output | The console output when running a tool is missing the "Observation" and "Thought" prefixes.
I noticed this when using the SQL Toolkit, but other tools are likely affected.
Here is the current INCORRECT output format:
```
> Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""invoice_... | https://github.com/langchain-ai/langchain/issues/3157 | https://github.com/langchain-ai/langchain/pull/3158 | 126d7f11dd17a8ea71a4427951f10cefc862ba3a | 0b542661b46d42ee501c6681a4519f2c4e76de23 | 2023-04-19T15:15:26Z | python | 2023-04-19T16:00:10Z | langchain/tools/base.py | self.callback_manager.on_tool_start(
{"name": self.name, "description": self.description},
tool_input if isinstance(tool_input, str) else str(tool_input),
verbose=verbose_,
color=start_color,
**kwargs,
)
try:
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,301 | Output using llamacpp is garbage | Hi there,
Trying to setup a langchain with llamacpp as a first step to use langchain offline:
`from langchain.llms import LlamaCpp
llm = LlamaCpp(model_path="../llama/models/ggml-vicuna-13b-4bit-rev1.bin")
text = "Question: What NFL team won the Super Bowl in the year Justin Bieber was born? Answer: Let's thi... | https://github.com/langchain-ai/langchain/issues/3301 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-21T14:01:59Z | python | 2023-04-23T01:46:55Z | langchain/llms/llamacpp.py | """Wrapper around llama.cpp."""
import logging
from typing import Any, Dict, List, Optional
from pydantic import Field, root_validator
from langchain.llms.base import LLM
logger = logging.getLogger(__name__)
class LlamaCpp(LLM): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,301 | Output using llamacpp is garbage | Hi there,
Trying to setup a langchain with llamacpp as a first step to use langchain offline:
`from langchain.llms import LlamaCpp
llm = LlamaCpp(model_path="../llama/models/ggml-vicuna-13b-4bit-rev1.bin")
text = "Question: What NFL team won the Super Bowl in the year Justin Bieber was born? Answer: Let's thi... | https://github.com/langchain-ai/langchain/issues/3301 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-21T14:01:59Z | python | 2023-04-23T01:46:55Z | langchain/llms/llamacpp.py | """Wrapper around the llama.cpp model.
To use, you should have the llama-cpp-python library installed, and provide the
path to the Llama model as a named parameter to the constructor.
Check out: https://github.com/abetlen/llama-cpp-python
Example:
.. code-block:: python
from langchai... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,301 | Output using llamacpp is garbage | Hi there,
Trying to setup a langchain with llamacpp as a first step to use langchain offline:
`from langchain.llms import LlamaCpp
llm = LlamaCpp(model_path="../llama/models/ggml-vicuna-13b-4bit-rev1.bin")
text = "Question: What NFL team won the Super Bowl in the year Justin Bieber was born? Answer: Let's thi... | https://github.com/langchain-ai/langchain/issues/3301 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-21T14:01:59Z | python | 2023-04-23T01:46:55Z | langchain/llms/llamacpp.py | use_mlock: bool = Field(False, alias="use_mlock")
"""Force system to keep model in RAM."""
n_threads: Optional[int] = Field(None, alias="n_threads")
"""Number of threads to use.
If None, the number of threads is automatically determined."""
n_batch: Optional[int] = Field(8, alias="n_batch")
"""... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,301 | Output using llamacpp is garbage | Hi there,
Trying to setup a langchain with llamacpp as a first step to use langchain offline:
`from langchain.llms import LlamaCpp
llm = LlamaCpp(model_path="../llama/models/ggml-vicuna-13b-4bit-rev1.bin")
text = "Question: What NFL team won the Super Bowl in the year Justin Bieber was born? Answer: Let's thi... | https://github.com/langchain-ai/langchain/issues/3301 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-21T14:01:59Z | python | 2023-04-23T01:46:55Z | langchain/llms/llamacpp.py | """Validate that llama-cpp-python library is installed."""
model_path = values["model_path"]
n_ctx = values["n_ctx"]
n_parts = values["n_parts"]
seed = values["seed"]
f16_kv = values["f16_kv"]
logits_all = values["logits_all"]
vocab_only = values["vocab_only"] |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,301 | Output using llamacpp is garbage | Hi there,
Trying to setup a langchain with llamacpp as a first step to use langchain offline:
`from langchain.llms import LlamaCpp
llm = LlamaCpp(model_path="../llama/models/ggml-vicuna-13b-4bit-rev1.bin")
text = "Question: What NFL team won the Super Bowl in the year Justin Bieber was born? Answer: Let's thi... | https://github.com/langchain-ai/langchain/issues/3301 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-21T14:01:59Z | python | 2023-04-23T01:46:55Z | langchain/llms/llamacpp.py | use_mlock = values["use_mlock"]
n_threads = values["n_threads"]
n_batch = values["n_batch"]
last_n_tokens_size = values["last_n_tokens_size"]
try:
from llama_cpp import Llama
values["client"] = Llama(
model_path=model_path,
n_ctx=n_... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,301 | Output using llamacpp is garbage | Hi there,
Trying to setup a langchain with llamacpp as a first step to use langchain offline:
`from langchain.llms import LlamaCpp
llm = LlamaCpp(model_path="../llama/models/ggml-vicuna-13b-4bit-rev1.bin")
text = "Question: What NFL team won the Super Bowl in the year Justin Bieber was born? Answer: Let's thi... | https://github.com/langchain-ai/langchain/issues/3301 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-21T14:01:59Z | python | 2023-04-23T01:46:55Z | langchain/llms/llamacpp.py | """Get the default parameters for calling llama_cpp."""
return {
"suffix": self.suffix,
"max_tokens": self.max_tokens,
"temperature": self.temperature,
"top_p": self.top_p,
"logprobs": self.logprobs,
"echo": self.echo,
"stop_seq... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,301 | Output using llamacpp is garbage | Hi there,
Trying to setup a langchain with llamacpp as a first step to use langchain offline:
`from langchain.llms import LlamaCpp
llm = LlamaCpp(model_path="../llama/models/ggml-vicuna-13b-4bit-rev1.bin")
text = "Question: What NFL team won the Super Bowl in the year Justin Bieber was born? Answer: Let's thi... | https://github.com/langchain-ai/langchain/issues/3301 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-21T14:01:59Z | python | 2023-04-23T01:46:55Z | langchain/llms/llamacpp.py | The generated text.
Example:
.. code-block:: python
from langchain.llms import LlamaCppEmbeddings
llm = LlamaCppEmbeddings(model_path="/path/to/local/llama/model.bin")
llm("This is a prompt.")
"""
params = self._default_params
i... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,241 | llama.cpp => model runs fine but bad output | Hi,
Windows 11 environement
Python: 3.10.11
I installed
- llama-cpp-python and it works fine and provides output
- transformers
- pytorch
Code run:
```
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
template = """Question: {question}
Answer: Let's think ste... | https://github.com/langchain-ai/langchain/issues/3241 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-20T20:36:45Z | python | 2023-04-23T01:46:55Z | langchain/llms/llamacpp.py | """Wrapper around llama.cpp."""
import logging
from typing import Any, Dict, List, Optional
from pydantic import Field, root_validator
from langchain.llms.base import LLM
logger = logging.getLogger(__name__)
class LlamaCpp(LLM): |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,241 | llama.cpp => model runs fine but bad output | Hi,
Windows 11 environement
Python: 3.10.11
I installed
- llama-cpp-python and it works fine and provides output
- transformers
- pytorch
Code run:
```
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
template = """Question: {question}
Answer: Let's think ste... | https://github.com/langchain-ai/langchain/issues/3241 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-20T20:36:45Z | python | 2023-04-23T01:46:55Z | langchain/llms/llamacpp.py | """Wrapper around the llama.cpp model.
To use, you should have the llama-cpp-python library installed, and provide the
path to the Llama model as a named parameter to the constructor.
Check out: https://github.com/abetlen/llama-cpp-python
Example:
.. code-block:: python
from langchai... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,241 | llama.cpp => model runs fine but bad output | Hi,
Windows 11 environement
Python: 3.10.11
I installed
- llama-cpp-python and it works fine and provides output
- transformers
- pytorch
Code run:
```
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
template = """Question: {question}
Answer: Let's think ste... | https://github.com/langchain-ai/langchain/issues/3241 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-20T20:36:45Z | python | 2023-04-23T01:46:55Z | langchain/llms/llamacpp.py | use_mlock: bool = Field(False, alias="use_mlock")
"""Force system to keep model in RAM."""
n_threads: Optional[int] = Field(None, alias="n_threads")
"""Number of threads to use.
If None, the number of threads is automatically determined."""
n_batch: Optional[int] = Field(8, alias="n_batch")
"""... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,241 | llama.cpp => model runs fine but bad output | Hi,
Windows 11 environement
Python: 3.10.11
I installed
- llama-cpp-python and it works fine and provides output
- transformers
- pytorch
Code run:
```
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
template = """Question: {question}
Answer: Let's think ste... | https://github.com/langchain-ai/langchain/issues/3241 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-20T20:36:45Z | python | 2023-04-23T01:46:55Z | langchain/llms/llamacpp.py | """Validate that llama-cpp-python library is installed."""
model_path = values["model_path"]
n_ctx = values["n_ctx"]
n_parts = values["n_parts"]
seed = values["seed"]
f16_kv = values["f16_kv"]
logits_all = values["logits_all"]
vocab_only = values["vocab_only"] |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,241 | llama.cpp => model runs fine but bad output | Hi,
Windows 11 environement
Python: 3.10.11
I installed
- llama-cpp-python and it works fine and provides output
- transformers
- pytorch
Code run:
```
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
template = """Question: {question}
Answer: Let's think ste... | https://github.com/langchain-ai/langchain/issues/3241 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-20T20:36:45Z | python | 2023-04-23T01:46:55Z | langchain/llms/llamacpp.py | use_mlock = values["use_mlock"]
n_threads = values["n_threads"]
n_batch = values["n_batch"]
last_n_tokens_size = values["last_n_tokens_size"]
try:
from llama_cpp import Llama
values["client"] = Llama(
model_path=model_path,
n_ctx=n_... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,241 | llama.cpp => model runs fine but bad output | Hi,
Windows 11 environement
Python: 3.10.11
I installed
- llama-cpp-python and it works fine and provides output
- transformers
- pytorch
Code run:
```
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
template = """Question: {question}
Answer: Let's think ste... | https://github.com/langchain-ai/langchain/issues/3241 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-20T20:36:45Z | python | 2023-04-23T01:46:55Z | langchain/llms/llamacpp.py | """Get the default parameters for calling llama_cpp."""
return {
"suffix": self.suffix,
"max_tokens": self.max_tokens,
"temperature": self.temperature,
"top_p": self.top_p,
"logprobs": self.logprobs,
"echo": self.echo,
"stop_seq... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,241 | llama.cpp => model runs fine but bad output | Hi,
Windows 11 environement
Python: 3.10.11
I installed
- llama-cpp-python and it works fine and provides output
- transformers
- pytorch
Code run:
```
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
template = """Question: {question}
Answer: Let's think ste... | https://github.com/langchain-ai/langchain/issues/3241 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-20T20:36:45Z | python | 2023-04-23T01:46:55Z | langchain/llms/llamacpp.py | The generated text.
Example:
.. code-block:: python
from langchain.llms import LlamaCppEmbeddings
llm = LlamaCppEmbeddings(model_path="/path/to/local/llama/model.bin")
llm("This is a prompt.")
"""
params = self._default_params
i... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,404 | marathon_times.ipynb: mismatched text and code | Text mentions inflation and tuition:
Here is the prompt comparing inflation and college tuition.
Code is about marathon times:
agent.run(["What were the winning boston marathon times for the past 5 years? Generate a table of the names, countries of origin, and times."]) | https://github.com/langchain-ai/langchain/issues/3404 | https://github.com/langchain-ai/langchain/pull/3408 | b4de839ed8a1bea7425a6923b2cd635068b6015a | 73bc70b4fa7bb69647d9dbe81943b88ce6ccc180 | 2023-04-23T21:06:49Z | python | 2023-04-24T01:14:11Z | langchain/tools/ddg_search/__init__.py | """DuckDuckGo Search API toolkit.""" |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,384 | ValueError in cosine_similarity when using FAISS index as vector store | Getting the below error
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "...\langchain\vectorstores\faiss.py", line 285, in max_marginal_relevance_search
docs = self.max_marginal_relevance_search_by_vector(embedding, k, fetch_k)
File "...\langchain\vectorstores\faiss.py... | https://github.com/langchain-ai/langchain/issues/3384 | https://github.com/langchain-ai/langchain/pull/3475 | 53b14de636080e09e128d829aafa9ea34ac34a94 | b2564a63911f8a77272ac9e93e5558384f00155c | 2023-04-23T07:51:56Z | python | 2023-04-25T02:54:15Z | langchain/math_utils.py | """Math utils."""
from typing import List, Union
import numpy as np
Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
"""Row-wise cosine similarity between two equal-width matrices."""
if len(X) == 0 or len(Y) == 0:
return np.array... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,384 | ValueError in cosine_similarity when using FAISS index as vector store | Getting the below error
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "...\langchain\vectorstores\faiss.py", line 285, in max_marginal_relevance_search
docs = self.max_marginal_relevance_search_by_vector(embedding, k, fetch_k)
File "...\langchain\vectorstores\faiss.py... | https://github.com/langchain-ai/langchain/issues/3384 | https://github.com/langchain-ai/langchain/pull/3475 | 53b14de636080e09e128d829aafa9ea34ac34a94 | b2564a63911f8a77272ac9e93e5558384f00155c | 2023-04-23T07:51:56Z | python | 2023-04-25T02:54:15Z | langchain/vectorstores/utils.py | """Utility functions for working with vectors and vectorstores."""
from typing import List
import numpy as np
from langchain.math_utils import cosine_similarity
def maximal_marginal_relevance( |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,384 | ValueError in cosine_similarity when using FAISS index as vector store | Getting the below error
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "...\langchain\vectorstores\faiss.py", line 285, in max_marginal_relevance_search
docs = self.max_marginal_relevance_search_by_vector(embedding, k, fetch_k)
File "...\langchain\vectorstores\faiss.py... | https://github.com/langchain-ai/langchain/issues/3384 | https://github.com/langchain-ai/langchain/pull/3475 | 53b14de636080e09e128d829aafa9ea34ac34a94 | b2564a63911f8a77272ac9e93e5558384f00155c | 2023-04-23T07:51:56Z | python | 2023-04-25T02:54:15Z | langchain/vectorstores/utils.py | query_embedding: np.ndarray,
embedding_list: list,
lambda_mult: float = 0.5,
k: int = 4,
) -> List[int]:
"""Calculate maximal marginal relevance."""
if min(k, len(embedding_list)) <= 0:
return []
similarity_to_query = cosine_similarity([query_embedding], embedding_list)[0]
most_simil... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,766 | Update poetry lock to allow SQLAlchemy v2 | It seems that #1578 adds support for SQLAlchemy v2 but the [poetry lock file](https://github.com/hwchase17/langchain/blob/8685d53adcdd0310e76349ecb4e2b87f980c4673/poetry.lock#L6211) is still at 1.4.46. | https://github.com/langchain-ai/langchain/issues/1766 | https://github.com/langchain-ai/langchain/pull/3310 | 7c2c73af5f15799c9326e99ed15c4a30fd19ad11 | b7658059643cd2f8fa58a2132b7d723638445ebc | 2023-03-19T01:48:23Z | python | 2023-04-25T04:10:56Z | langchain/sql_database.py | """SQLAlchemy wrapper around a database."""
from __future__ import annotations
import warnings
from typing import Any, Iterable, List, Optional
from sqlalchemy import MetaData, Table, create_engine, inspect, select, text
from sqlalchemy.engine import Engine
from sqlalchemy.exc import ProgrammingError, SQLAlchemyError
f... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,766 | Update poetry lock to allow SQLAlchemy v2 | It seems that #1578 adds support for SQLAlchemy v2 but the [poetry lock file](https://github.com/hwchase17/langchain/blob/8685d53adcdd0310e76349ecb4e2b87f980c4673/poetry.lock#L6211) is still at 1.4.46. | https://github.com/langchain-ai/langchain/issues/1766 | https://github.com/langchain-ai/langchain/pull/3310 | 7c2c73af5f15799c9326e99ed15c4a30fd19ad11 | b7658059643cd2f8fa58a2132b7d723638445ebc | 2023-03-19T01:48:23Z | python | 2023-04-25T04:10:56Z | langchain/sql_database.py | if include_tables and ignore_tables:
raise ValueError("Cannot specify both include_tables and ignore_tables")
self._inspector = inspect(self._engine)
self._all_tables = set(
self._inspector.get_table_names(schema=schema)
+ (self._inspector.get_view_n... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,766 | Update poetry lock to allow SQLAlchemy v2 | It seems that #1578 adds support for SQLAlchemy v2 but the [poetry lock file](https://github.com/hwchase17/langchain/blob/8685d53adcdd0310e76349ecb4e2b87f980c4673/poetry.lock#L6211) is still at 1.4.46. | https://github.com/langchain-ai/langchain/issues/1766 | https://github.com/langchain-ai/langchain/pull/3310 | 7c2c73af5f15799c9326e99ed15c4a30fd19ad11 | b7658059643cd2f8fa58a2132b7d723638445ebc | 2023-03-19T01:48:23Z | python | 2023-04-25T04:10:56Z | langchain/sql_database.py | if self._custom_table_info:
if not isinstance(self._custom_table_info, dict):
raise TypeError(
"table_info must be a dictionary with table names as keys and the "
"desired table info as values"
)
intersection = ... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,766 | Update poetry lock to allow SQLAlchemy v2 | It seems that #1578 adds support for SQLAlchemy v2 but the [poetry lock file](https://github.com/hwchase17/langchain/blob/8685d53adcdd0310e76349ecb4e2b87f980c4673/poetry.lock#L6211) is still at 1.4.46. | https://github.com/langchain-ai/langchain/issues/1766 | https://github.com/langchain-ai/langchain/pull/3310 | 7c2c73af5f15799c9326e99ed15c4a30fd19ad11 | b7658059643cd2f8fa58a2132b7d723638445ebc | 2023-03-19T01:48:23Z | python | 2023-04-25T04:10:56Z | langchain/sql_database.py | """Return string representation of dialect to use."""
return self._engine.dialect.name
def get_usable_table_names(self) -> Iterable[str]:
"""Get names of tables available."""
if self._include_tables:
return self._include_tables
return self._all_tables - self._ignore_table... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,766 | Update poetry lock to allow SQLAlchemy v2 | It seems that #1578 adds support for SQLAlchemy v2 but the [poetry lock file](https://github.com/hwchase17/langchain/blob/8685d53adcdd0310e76349ecb4e2b87f980c4673/poetry.lock#L6211) is still at 1.4.46. | https://github.com/langchain-ai/langchain/issues/1766 | https://github.com/langchain-ai/langchain/pull/3310 | 7c2c73af5f15799c9326e99ed15c4a30fd19ad11 | b7658059643cd2f8fa58a2132b7d723638445ebc | 2023-03-19T01:48:23Z | python | 2023-04-25T04:10:56Z | langchain/sql_database.py | all_table_names = table_names
meta_tables = [
tbl
for tbl in self._metadata.sorted_tables
if tbl.name in set(all_table_names)
and not (self.dialect == "sqlite" and tbl.name.startswith("sqlite_"))
]
tables = []
for table in meta_tables:
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,766 | Update poetry lock to allow SQLAlchemy v2 | It seems that #1578 adds support for SQLAlchemy v2 but the [poetry lock file](https://github.com/hwchase17/langchain/blob/8685d53adcdd0310e76349ecb4e2b87f980c4673/poetry.lock#L6211) is still at 1.4.46. | https://github.com/langchain-ai/langchain/issues/1766 | https://github.com/langchain-ai/langchain/pull/3310 | 7c2c73af5f15799c9326e99ed15c4a30fd19ad11 | b7658059643cd2f8fa58a2132b7d723638445ebc | 2023-03-19T01:48:23Z | python | 2023-04-25T04:10:56Z | langchain/sql_database.py | indexes = self._inspector.get_indexes(table.name)
indexes_formatted = "\n".join(map(_format_index, indexes))
return f"Table Indexes:\n{indexes_formatted}"
def _get_sample_rows(self, table: Table) -> str:
command = select([table]).limit(self._sample_rows_in_table_info)
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,766 | Update poetry lock to allow SQLAlchemy v2 | It seems that #1578 adds support for SQLAlchemy v2 but the [poetry lock file](https://github.com/hwchase17/langchain/blob/8685d53adcdd0310e76349ecb4e2b87f980c4673/poetry.lock#L6211) is still at 1.4.46. | https://github.com/langchain-ai/langchain/issues/1766 | https://github.com/langchain-ai/langchain/pull/3310 | 7c2c73af5f15799c9326e99ed15c4a30fd19ad11 | b7658059643cd2f8fa58a2132b7d723638445ebc | 2023-03-19T01:48:23Z | python | 2023-04-25T04:10:56Z | langchain/sql_database.py | """Execute a SQL command and return a string representing the results.
If the statement returns rows, a string of the results is returned.
If the statement returns no rows, an empty string is returned.
"""
with self._engine.begin() as connection:
if self._schema is not None:
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,766 | Update poetry lock to allow SQLAlchemy v2 | It seems that #1578 adds support for SQLAlchemy v2 but the [poetry lock file](https://github.com/hwchase17/langchain/blob/8685d53adcdd0310e76349ecb4e2b87f980c4673/poetry.lock#L6211) is still at 1.4.46. | https://github.com/langchain-ai/langchain/issues/1766 | https://github.com/langchain-ai/langchain/pull/3310 | 7c2c73af5f15799c9326e99ed15c4a30fd19ad11 | b7658059643cd2f8fa58a2132b7d723638445ebc | 2023-03-19T01:48:23Z | python | 2023-04-25T04:10:56Z | langchain/sql_database.py | """Get information about specified tables.
Follows best practices as specified in: Rajkumar et al, 2022
(https://arxiv.org/abs/2204.00498)
If `sample_rows_in_table_info`, the specified number of sample rows will be
appended to each table description. This can increase performance as
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown... | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z | langchain/vectorstores/weaviate.py | """Wrapper around weaviate vector database."""
from __future__ import annotations
from typing import Any, Dict, Iterable, List, Optional, Type
from uuid import uuid4
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown... | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z | langchain/vectorstores/weaviate.py | client = kwargs.get("client")
if client is not None:
return client
weaviate_url = get_from_dict_or_env(kwargs, "weaviate_url", "WEAVIATE_URL")
weaviate_api_key = get_from_dict_or_env(
kwargs, "weaviate_api_key", "WEAVIATE_API_KEY", None
)
try:
import weaviate
except Impor... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown... | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z | langchain/vectorstores/weaviate.py | weaviate = Weaviate(client, index_name, text_key)
"""
def __init__(
self,
client: Any,
index_name: str,
text_key: str,
embedding: Optional[Embeddings] = None,
attributes: Optional[List[str]] = None,
):
"""Initialize with Weaviate client."""
try... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown... | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z | langchain/vectorstores/weaviate.py | self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Upload texts with metadata (properties) to Weaviate."""
from weaviate.util import get_valid_uuid
with self._client.batch as batch:
ids = []
fo... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown... | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z | langchain/vectorstores/weaviate.py | self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown... | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z | langchain/vectorstores/weaviate.py | self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Look up similar documents by embedding vector in Weaviate."""
vector = {"vector": embedding}
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if kwargs.get("where_filter"):
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown... | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z | langchain/vectorstores/weaviate.py | fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown... | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z | langchain/vectorstores/weaviate.py | k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown... | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z | langchain/vectorstores/weaviate.py | return docs
@classmethod
def from_texts(
cls: Type[Weaviate],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> Weaviate:
"""Construct Weaviate wrapper from raw documents.
This is a user-friendly inter... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown... | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z | langchain/vectorstores/weaviate.py | text_key = "text"
schema = _default_schema(index_name)
attributes = list(metadatas[0].keys()) if metadatas else None
if not client.schema.contains(schema):
client.schema.create_class(schema)
with client.batch as batch:
for i, text in enumerate(texts):
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown... | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z | tests/integration_tests/vectorstores/test_weaviate.py | """Test Weaviate functionality."""
import logging
import os
from typing import Generator, Union
import pytest
from weaviate import Client
from langchain.docstore.document import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.weaviate import Weaviate
logging.basicConfig(lev... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown... | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z | tests/integration_tests/vectorstores/test_weaviate.py | self, weaviate_url: str, embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end construction and search without metadata."""
texts = ["foo", "bar", "baz"]
docsearch = Weaviate.from_texts(
texts,
embedding_openai,
weaviate_url=weaviate_url,
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown... | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z | tests/integration_tests/vectorstores/test_weaviate.py | self, weaviate_url: str, embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end construction and search with metadata."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Weaviate.from_texts(
texts, embedding_openai,... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown... | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z | tests/integration_tests/vectorstores/test_weaviate.py | output = docsearch.max_marginal_relevance_search(
"foo", k=2, fetch_k=3, lambda_mult=0.0
)
assert output == [
Document(page_content="foo", metadata={"page": 0}),
Document(page_content="bar", metadata={"page": 1}),
]
@pytest.mark.vcr(ignore_localhost=True)
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown... | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z | tests/integration_tests/vectorstores/test_weaviate.py | Document(page_content="foo", metadata={"page": 0}),
Document(page_content="bar", metadata={"page": 1}),
]
@pytest.mark.vcr(ignore_localhost=True)
def test_max_marginal_relevance_search_with_filter(
self, weaviate_url: str, embedding_openai: OpenAIEmbeddings
) -> None:
"""... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,664 | import error when importing `from langchain import OpenAI` on 0.0.151 | got the following error when running today:
``` File "venv/lib/python3.11/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "venv/lib/python3.11/site-packages/langchain/agents/__init__.py", line 2, in <module>
from l... | https://github.com/langchain-ai/langchain/issues/3664 | https://github.com/langchain-ai/langchain/pull/3667 | 708787dddb2fa3cdb2d1dabefa00c01ffec572f6 | 1b5721c999c9fc310cefec383666f43c80ec9620 | 2023-04-27T16:24:30Z | python | 2023-04-27T18:39:01Z | langchain/utilities/bash.py | """Wrapper around subprocess to run commands."""
import re
import subprocess
from typing import List, Union
from uuid import uuid4
import pexpect
class BashProcess: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,664 | import error when importing `from langchain import OpenAI` on 0.0.151 | got the following error when running today:
``` File "venv/lib/python3.11/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "venv/lib/python3.11/site-packages/langchain/agents/__init__.py", line 2, in <module>
from l... | https://github.com/langchain-ai/langchain/issues/3664 | https://github.com/langchain-ai/langchain/pull/3667 | 708787dddb2fa3cdb2d1dabefa00c01ffec572f6 | 1b5721c999c9fc310cefec383666f43c80ec9620 | 2023-04-27T16:24:30Z | python | 2023-04-27T18:39:01Z | langchain/utilities/bash.py | """Executes bash commands and returns the output."""
def __init__(
self,
strip_newlines: bool = False,
return_err_output: bool = False,
persistent: bool = False,
):
"""Initialize with stripping newlines."""
self.strip_newlines = strip_newlines
self.return_... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,664 | import error when importing `from langchain import OpenAI` on 0.0.151 | got the following error when running today:
``` File "venv/lib/python3.11/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "venv/lib/python3.11/site-packages/langchain/agents/__init__.py", line 2, in <module>
from l... | https://github.com/langchain-ai/langchain/issues/3664 | https://github.com/langchain-ai/langchain/pull/3667 | 708787dddb2fa3cdb2d1dabefa00c01ffec572f6 | 1b5721c999c9fc310cefec383666f43c80ec9620 | 2023-04-27T16:24:30Z | python | 2023-04-27T18:39:01Z | langchain/utilities/bash.py | """Run commands and return final output."""
if isinstance(commands, str):
commands = [commands]
commands = ";".join(commands)
if self.process is not None:
return self._run_persistent(
commands,
)
else:
return self._run(comma... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,664 | import error when importing `from langchain import OpenAI` on 0.0.151 | got the following error when running today:
``` File "venv/lib/python3.11/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "venv/lib/python3.11/site-packages/langchain/agents/__init__.py", line 2, in <module>
from l... | https://github.com/langchain-ai/langchain/issues/3664 | https://github.com/langchain-ai/langchain/pull/3667 | 708787dddb2fa3cdb2d1dabefa00c01ffec572f6 | 1b5721c999c9fc310cefec383666f43c80ec9620 | 2023-04-27T16:24:30Z | python | 2023-04-27T18:39:01Z | langchain/utilities/bash.py | pattern = re.escape(command) + r"\s*\n"
output = re.sub(pattern, "", output, count=1)
return output.strip()
def _run_persistent(self, command: str) -> str:
"""Run commands and return final output."""
if self.process is None:
raise ValueError("Process not initialized")
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | langchain/vectorstores/chroma.py | """Wrapper around ChromaDB embeddings platform."""
from __future__ import annotations
import logging
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from la... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | langchain/vectorstores/chroma.py | return [doc for doc, _ in _results_to_docs_and_scores(results)]
def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]:
return [
(Document(page_content=result[0], metadata=result[1] or {}), result[2])
for result in zip(
results["documents"][0],
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | langchain/vectorstores/chroma.py | self,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
embedding_function: Optional[Embeddings] = None,
persist_directory: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
collection_metadata: Optional[Dict] = None,
client: Optio... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | langchain/vectorstores/chroma.py | import chromadb.config
except ImportError:
raise ValueError(
"Could not import chromadb python package. "
"Please install it with `pip install chromadb`."
)
if client is not None:
self._client = client
else:
if clien... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | langchain/vectorstores/chroma.py | self,
query_texts: Optional[List[str]] = None,
query_embeddings: Optional[List[List[float]]] = None,
n_results: int = 4,
where: Optional[Dict[str, str]] = None,
) -> List[Document]:
"""Query the chroma collection."""
for i in range(n_results, 0, -1):
try:
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | langchain/vectorstores/chroma.py | self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts (Iterable[str]): Texts to add to the vector... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | langchain/vectorstores/chroma.py | self,
query: str,
k: int = 4,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Run similarity search with Chroma.
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | langchain/vectorstores/chroma.py | self,
embedding: List[float],
k: int = 4,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Doc... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | langchain/vectorstores/chroma.py | self,
query: str,
k: int = 4,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Run similarity search with Chroma with distance.
Args:
query (str): Query text to search for.
k (int): Number of results... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | langchain/vectorstores/chroma.py | query_embeddings=[query_embedding], n_results=k, where=filter
)
return _results_to_docs_and_scores(results)
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: ... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | langchain/vectorstores/chroma.py | where=filter,
include=["metadatas", "documents", "distances", "embeddings"],
)
mmr_selected = maximal_marginal_relevance(
np.array(embedding, dtype=np.float32),
results["embeddings"][0],
k=k,
lambda_mult=lambda_mult,
)
candidate... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | langchain/vectorstores/chroma.py | to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents selected by maximal marginal relevance.
"""
if self._embedding_function is None:
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | langchain/vectorstores/chroma.py | """Update a document in the collection.
Args:
document_id (str): ID of the document to update.
document (Document): Document to update.
"""
text = document.page_content
metadata = document.metadata
self._collection.update_document(document_id, text, metada... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | langchain/vectorstores/chroma.py | If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Args:
texts (List[str]): List of texts to add to the collection.
collection_name (str): Name of the collection to create.
persist_directory (O... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | langchain/vectorstores/chroma.py | client_settings: Optional[chromadb.config.Settings] = None,
client: Optional[chromadb.Client] = None,
**kwargs: Any,
) -> Chroma:
"""Create a Chroma vectorstore from a list of documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise,... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | tests/integration_tests/vectorstores/test_chroma.py | """Test Chroma functionality."""
import pytest
from langchain.docstore.document import Document
from langchain.vectorstores import Chroma
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_chroma() -> None: |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | tests/integration_tests/vectorstores/test_chroma.py | """Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Chroma.from_texts(
collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
@pytest.ma... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | tests/integration_tests/vectorstores/test_chroma.py | """Test end to end construction and scored search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
)
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | tests/integration_tests/vectorstores/test_chroma.py | """Test end to end construction and scored search with metadata filtering."""
texts = ["far", "bar", "baz"]
metadatas = [{"first_letter": "{}".format(text[0])} for text in texts]
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings()... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_rel... | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z | tests/integration_tests/vectorstores/test_chroma.py | """Test end to end construction and search, with persistence."""
chroma_persist_dir = "./tests/persist_dir"
collection_name = "test_collection"
texts = ["foo", "bar", "baz"]
docsearch = Chroma.from_texts(
collection_name=collection_name,
texts=texts,
embedding=FakeEmbeddings(),
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,988 | LangChain openAI callback doesn't allow finetuned models | Hi all!
I have an [application](https://github.com/ur-whitelab/BO-LIFT) based on langchain.
A few months ago, I used it with fine-tuned (FT) models.
We added a token usage counter later, and I haven't tried fine-tuned models again since then.
Recently we have been interested in using (FT) models again, but the ... | https://github.com/langchain-ai/langchain/issues/3988 | https://github.com/langchain-ai/langchain/pull/4009 | aa383559999b3d6a781c62ed7f8589fef8892879 | f08a76250fe8995fb3f05bf785677070922d4b0d | 2023-05-02T18:00:22Z | python | 2023-05-02T23:19:57Z | langchain/callbacks/openai_info.py | """Callback Handler that prints to std out."""
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import AgentAction, AgentFinish, LLMResult
def get_openai_model_cost_per_1k_tokens(
model_name: str, is_completion: bool = False
) -> floa... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,988 | LangChain openAI callback doesn't allow finetuned models | Hi all!
I have an [application](https://github.com/ur-whitelab/BO-LIFT) based on langchain.
A few months ago, I used it with fine-tuned (FT) models.
We added a token usage counter later, and I haven't tried fine-tuned models again since then.
Recently we have been interested in using (FT) models again, but the ... | https://github.com/langchain-ai/langchain/issues/3988 | https://github.com/langchain-ai/langchain/pull/4009 | aa383559999b3d6a781c62ed7f8589fef8892879 | f08a76250fe8995fb3f05bf785677070922d4b0d | 2023-05-02T18:00:22Z | python | 2023-05-02T23:19:57Z | langchain/callbacks/openai_info.py | model_name.lower()
+ ("-completion" if is_completion and model_name.startswith("gpt-4") else ""),
None,
)
if cost is None:
raise ValueError(
f"Unknown model: {model_name}. Please provide a valid OpenAI model name."
"Known models are: " + ", ".join(model_cost_mappi... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,988 | LangChain openAI callback doesn't allow finetuned models | Hi all!
I have an [application](https://github.com/ur-whitelab/BO-LIFT) based on langchain.
A few months ago, I used it with fine-tuned (FT) models.
We added a token usage counter later, and I haven't tried fine-tuned models again since then.
Recently we have been interested in using (FT) models again, but the ... | https://github.com/langchain-ai/langchain/issues/3988 | https://github.com/langchain-ai/langchain/pull/4009 | aa383559999b3d6a781c62ed7f8589fef8892879 | f08a76250fe8995fb3f05bf785677070922d4b0d | 2023-05-02T18:00:22Z | python | 2023-05-02T23:19:57Z | langchain/callbacks/openai_info.py | self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""Print out the prompts."""
pass
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""Print out the token."""
pass
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,988 | LangChain openAI callback doesn't allow finetuned models | Hi all!
I have an [application](https://github.com/ur-whitelab/BO-LIFT) based on langchain.
A few months ago, I used it with fine-tuned (FT) models.
We added a token usage counter later, and I haven't tried fine-tuned models again since then.
Recently we have been interested in using (FT) models again, but the ... | https://github.com/langchain-ai/langchain/issues/3988 | https://github.com/langchain-ai/langchain/pull/4009 | aa383559999b3d6a781c62ed7f8589fef8892879 | f08a76250fe8995fb3f05bf785677070922d4b0d | 2023-05-02T18:00:22Z | python | 2023-05-02T23:19:57Z | langchain/callbacks/openai_info.py | self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Do nothing."""
pass
def on_chain_start(
self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
) -> None:
"""Print out that we are entering a chain."""
pass
def on_chain... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,988 | LangChain openAI callback doesn't allow finetuned models | Hi all!
I have an [application](https://github.com/ur-whitelab/BO-LIFT) based on langchain.
A few months ago, I used it with fine-tuned (FT) models.
We added a token usage counter later, and I haven't tried fine-tuned models again since then.
Recently we have been interested in using (FT) models again, but the ... | https://github.com/langchain-ai/langchain/issues/3988 | https://github.com/langchain-ai/langchain/pull/4009 | aa383559999b3d6a781c62ed7f8589fef8892879 | f08a76250fe8995fb3f05bf785677070922d4b0d | 2023-05-02T18:00:22Z | python | 2023-05-02T23:19:57Z | langchain/callbacks/openai_info.py | self,
output: str,
color: Optional[str] = None,
observation_prefix: Optional[str] = None,
llm_prefix: Optional[str] = None,
**kwargs: Any,
) -> None:
"""If not the final action, print out observation."""
pass
def on_tool_error(
self, error: Union[E... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,053 | Tools with partials (Partial functions not yet supported in tools) | We commonly used this pattern to create tools:
```py
from langchain.tools import Tool
from functools import partial
def foo(x, y):
return y
Tool.from_function(
func=partial(foo, "bar"),
name = "foo",
description="foobar"
)
```
which as of 0.0.148 (I think) gives a pydantic error "Par... | https://github.com/langchain-ai/langchain/issues/4053 | https://github.com/langchain-ai/langchain/pull/4058 | 7e967aa4d581bec8b29e9ea44267505b0bad18b9 | afa9d1292b0a152e36d338dde7b02f0b93bd37d9 | 2023-05-03T17:28:46Z | python | 2023-05-03T20:16:41Z | langchain/tools/base.py | """Base implementation for tools or skills."""
from __future__ import annotations
import warnings
from abc import ABC, abstractmethod
from functools import partial
from inspect import signature
from typing import Any, Awaitable, Callable, Dict, Optional, Tuple, Type, Union
from pydantic import (
BaseModel,
Extr... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,053 | Tools with partials (Partial functions not yet supported in tools) | We commonly used this pattern to create tools:
```py
from langchain.tools import Tool
from functools import partial
def foo(x, y):
return y
Tool.from_function(
func=partial(foo, "bar"),
name = "foo",
description="foobar"
)
```
which as of 0.0.148 (I think) gives a pydantic error "Par... | https://github.com/langchain-ai/langchain/issues/4053 | https://github.com/langchain-ai/langchain/pull/4058 | 7e967aa4d581bec8b29e9ea44267505b0bad18b9 | afa9d1292b0a152e36d338dde7b02f0b93bd37d9 | 2023-05-03T17:28:46Z | python | 2023-05-03T20:16:41Z | langchain/tools/base.py | """Metaclass for BaseTool to ensure the provided args_schema
doesn't silently ignored."""
def __new__(
cls: Type[ToolMetaclass], name: str, bases: Tuple[Type, ...], dct: dict
) -> ToolMetaclass:
"""Create the definition of the new tool class."""
schema_type: Optional[Type[BaseModel]]... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,053 | Tools with partials (Partial functions not yet supported in tools) | We commonly used this pattern to create tools:
```py
from langchain.tools import Tool
from functools import partial
def foo(x, y):
return y
Tool.from_function(
func=partial(foo, "bar"),
name = "foo",
description="foobar"
)
```
which as of 0.0.148 (I think) gives a pydantic error "Par... | https://github.com/langchain-ai/langchain/issues/4053 | https://github.com/langchain-ai/langchain/pull/4058 | 7e967aa4d581bec8b29e9ea44267505b0bad18b9 | afa9d1292b0a152e36d338dde7b02f0b93bd37d9 | 2023-05-03T17:28:46Z | python | 2023-05-03T20:16:41Z | langchain/tools/base.py | name: str, model: BaseModel, field_names: list
) -> Type[BaseModel]:
"""Create a pydantic model with only a subset of model's fields."""
fields = {
field_name: (
model.__fields__[field_name].type_,
model.__fields__[field_name].default,
)
for field_name in field_na... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,053 | Tools with partials (Partial functions not yet supported in tools) | We commonly used this pattern to create tools:
```py
from langchain.tools import Tool
from functools import partial
def foo(x, y):
return y
Tool.from_function(
func=partial(foo, "bar"),
name = "foo",
description="foobar"
)
```
which as of 0.0.148 (I think) gives a pydantic error "Par... | https://github.com/langchain-ai/langchain/issues/4053 | https://github.com/langchain-ai/langchain/pull/4058 | 7e967aa4d581bec8b29e9ea44267505b0bad18b9 | afa9d1292b0a152e36d338dde7b02f0b93bd37d9 | 2023-05-03T17:28:46Z | python | 2023-05-03T20:16:41Z | langchain/tools/base.py | """Configuration for the pydantic model."""
extra = Extra.forbid
arbitrary_types_allowed = True
def create_schema_from_function(
model_name: str,
func: Callable,
) -> Type[BaseModel]:
"""Create a pydantic schema from a function's signature."""
validated = validate_arguments(func, config=_SchemaC... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,053 | Tools with partials (Partial functions not yet supported in tools) | We commonly used this pattern to create tools:
```py
from langchain.tools import Tool
from functools import partial
def foo(x, y):
return y
Tool.from_function(
func=partial(foo, "bar"),
name = "foo",
description="foobar"
)
```
which as of 0.0.148 (I think) gives a pydantic error "Par... | https://github.com/langchain-ai/langchain/issues/4053 | https://github.com/langchain-ai/langchain/pull/4058 | 7e967aa4d581bec8b29e9ea44267505b0bad18b9 | afa9d1292b0a152e36d338dde7b02f0b93bd37d9 | 2023-05-03T17:28:46Z | python | 2023-05-03T20:16:41Z | langchain/tools/base.py | """Interface LangChain tools must implement."""
name: str
"""The unique name of the tool that clearly communicates its purpose."""
description: str
"""Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
"""
args_schema: Op... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,053 | Tools with partials (Partial functions not yet supported in tools) | We commonly used this pattern to create tools:
```py
from langchain.tools import Tool
from functools import partial
def foo(x, y):
return y
Tool.from_function(
func=partial(foo, "bar"),
name = "foo",
description="foobar"
)
```
which as of 0.0.148 (I think) gives a pydantic error "Par... | https://github.com/langchain-ai/langchain/issues/4053 | https://github.com/langchain-ai/langchain/pull/4058 | 7e967aa4d581bec8b29e9ea44267505b0bad18b9 | afa9d1292b0a152e36d338dde7b02f0b93bd37d9 | 2023-05-03T17:28:46Z | python | 2023-05-03T20:16:41Z | langchain/tools/base.py | """Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def is_single_input(self) -> bool:
"""Whether the tool only accepts a single input."""
return len(self.args) == 1
@property
def args(self) -> dict:
if self.... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,053 | Tools with partials (Partial functions not yet supported in tools) | We commonly used this pattern to create tools:
```py
from langchain.tools import Tool
from functools import partial
def foo(x, y):
return y
Tool.from_function(
func=partial(foo, "bar"),
name = "foo",
description="foobar"
)
```
which as of 0.0.148 (I think) gives a pydantic error "Par... | https://github.com/langchain-ai/langchain/issues/4053 | https://github.com/langchain-ai/langchain/pull/4058 | 7e967aa4d581bec8b29e9ea44267505b0bad18b9 | afa9d1292b0a152e36d338dde7b02f0b93bd37d9 | 2023-05-03T17:28:46Z | python | 2023-05-03T20:16:41Z | langchain/tools/base.py | """Raise deprecation warning if callback_manager is used."""
if values.get("callback_manager") is not None:
warnings.warn(
"callback_manager is deprecated. Please use callbacks instead.",
DeprecationWarning,
)
values["callbacks"] = values.pop("... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,053 | Tools with partials (Partial functions not yet supported in tools) | We commonly used this pattern to create tools:
```py
from langchain.tools import Tool
from functools import partial
def foo(x, y):
return y
Tool.from_function(
func=partial(foo, "bar"),
name = "foo",
description="foobar"
)
```
which as of 0.0.148 (I think) gives a pydantic error "Par... | https://github.com/langchain-ai/langchain/issues/4053 | https://github.com/langchain-ai/langchain/pull/4058 | 7e967aa4d581bec8b29e9ea44267505b0bad18b9 | afa9d1292b0a152e36d338dde7b02f0b93bd37d9 | 2023-05-03T17:28:46Z | python | 2023-05-03T20:16:41Z | langchain/tools/base.py | if isinstance(tool_input, str):
return (tool_input,), {}
else:
return (), tool_input
def run(
self,
tool_input: Union[str, Dict],
verbose: Optional[bool] = None,
start_color: Optional[str] = "green",
color: Optional[str] = "green",
call... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,053 | Tools with partials (Partial functions not yet supported in tools) | We commonly used this pattern to create tools:
```py
from langchain.tools import Tool
from functools import partial
def foo(x, y):
return y
Tool.from_function(
func=partial(foo, "bar"),
name = "foo",
description="foobar"
)
```
which as of 0.0.148 (I think) gives a pydantic error "Par... | https://github.com/langchain-ai/langchain/issues/4053 | https://github.com/langchain-ai/langchain/pull/4058 | 7e967aa4d581bec8b29e9ea44267505b0bad18b9 | afa9d1292b0a152e36d338dde7b02f0b93bd37d9 | 2023-05-03T17:28:46Z | python | 2023-05-03T20:16:41Z | langchain/tools/base.py | tool_input if isinstance(tool_input, str) else str(tool_input),
color=start_color,
**kwargs,
)
try:
tool_args, tool_kwargs = self._to_args_and_kwargs(tool_input)
observation = (
self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
... |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,053 | Tools with partials (Partial functions not yet supported in tools) | We commonly used this pattern to create tools:
```py
from langchain.tools import Tool
from functools import partial
def foo(x, y):
return y
Tool.from_function(
func=partial(foo, "bar"),
name = "foo",
description="foobar"
)
```
which as of 0.0.148 (I think) gives a pydantic error "Par... | https://github.com/langchain-ai/langchain/issues/4053 | https://github.com/langchain-ai/langchain/pull/4058 | 7e967aa4d581bec8b29e9ea44267505b0bad18b9 | afa9d1292b0a152e36d338dde7b02f0b93bd37d9 | 2023-05-03T17:28:46Z | python | 2023-05-03T20:16:41Z | langchain/tools/base.py | verbose_ = self.verbose
callback_manager = AsyncCallbackManager.configure(
callbacks, self.callbacks, verbose=verbose_
)
new_arg_supported = signature(self._arun).parameters.get("run_manager")
run_manager = await callback_manager.on_tool_start(
{"name": self.name,... |
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