Merge pull request #2766 from xtekky/27Feb

Add langchain integration

docs/pydantic_ai.md

@@ -21,10 +21,10 @@ pip install g4f pydantic_ai
 
 ### 1. Patch PydanticAI to Use G4F Models
 
-In order to use PydanticAI with G4F models, you need to apply the necessary patch to the client. This can be done by importing `patch_infer_model` from `g4f.tools.pydantic_ai`. The `api_key` parameter is optional, so if you have one, you can provide it. If not, the system will proceed without it.
+In order to use PydanticAI with G4F models, you need to apply the necessary patch to the client. This can be done by importing `patch_infer_model` from `g4f.integration.pydantic_ai`. The `api_key` parameter is optional, so if you have one, you can provide it. If not, the system will proceed without it.
 
 ```python
-from g4f.tools.pydantic_ai import patch_infer_model
+from g4f.integration.pydantic_ai import patch_infer_model
 
 patch_infer_model(api_key="your_api_key_here")  # Optional
 ```
@@ -89,7 +89,7 @@ For example, you can process your query or interact with external systems before
 
 ```python
 from pydantic_ai import Agent
-from g4f.tools.pydantic_ai import AIModel
+from g4f.integration.pydantic_ai import AIModel
 
 agent = Agent(
     AIModel("gpt-4o"),
@@ -109,7 +109,7 @@ This example shows how to initialize an agent with a specific model (`gpt-4o`) a
 from pydantic import BaseModel
 from pydantic_ai import Agent
 from pydantic_ai.models import ModelSettings
-from g4f.tools.pydantic_ai import patch_infer_model
+from g4f.integration.pydantic_ai import patch_infer_model
 
 patch_infer_model("your_api_key")
 
@@ -129,6 +129,39 @@ This example demonstrates the use of a custom Pydantic model (`MyModel`) to capt
 
 ---
 
+## LangChain Integration Example
+
+For users working with LangChain, here is an example demonstrating how to integrate G4F models into a LangChain environment:
+
+```python
+from g4f.integration.langchain import ChatAI
+import g4f.debug
+
+# Enable debugging logs
+g4f.debug.logging = True
+
+llm = ChatAI(
+    model="llama3-70b-8192",
+    provider="Groq",
+    api_key=""  # Optionally add your API key here
+)
+
+messages = [
+    {"role": "user", "content": "2 🦜 2"},
+    {"role": "assistant", "content": "4 🦜"},
+    {"role": "user", "content": "2 🦜 3"},
+    {"role": "assistant", "content": "5 🦜"},
+    {"role": "user", "content": "3 🦜 4"},
+]
+
+response = llm.invoke(messages)
+assert(response.content == "7 🦜")
+```
+
+This example shows how to use LangChain's `ChatAI` integration to create a conversational agent with a G4F model. The interaction takes place with the given messages and the agent processes them step-by-step to return the expected output.
+
+---
+
 ## Conclusion
 
 By following these steps, you have successfully integrated PydanticAI models into the G4F client, created an agent, and enabled debugging. This allows you to conduct conversations with the language model, pass system prompts, and retrieve responses synchronously.

g4f/Provider/PollinationsAI.py

@@ -14,7 +14,7 @@
 from ..errors import ModelNotFoundError
 from ..requests.raise_for_status import raise_for_status
 from ..requests.aiohttp import get_connector
-from ..providers.response import ImageResponse, ImagePreview, FinishReason, Usage, Reasoning
+from ..providers.response import ImageResponse, ImagePreview, FinishReason, Usage
 
 DEFAULT_HEADERS = {
     'Accept': '*/*',
@@ -42,7 +42,7 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
     text_models = [default_model]
     image_models = [default_image_model]
     extra_image_models = ["flux-pro", "flux-dev", "flux-schnell", "midjourney", "dall-e-3"]
-    vision_models = [default_vision_model, "gpt-4o-mini"]
+    vision_models = [default_vision_model, "gpt-4o-mini", "o1-mini"]
     extra_text_models = ["claude", "claude-email", "deepseek-reasoner", "deepseek-r1"] + vision_models
     _models_loaded = False
     model_aliases = {
@@ -53,16 +53,14 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
         "qwen-2.5-coder-32b": "qwen-coder",
         "llama-3.3-70b": "llama",
         "mistral-nemo": "mistral",
-        "gpt-4o-mini": "rtist",
         "gpt-4o": "searchgpt",
-        "gpt-4o-mini": "p1",
         "deepseek-chat": "claude-hybridspace",
         "llama-3.1-8b": "llamalight",
         "gpt-4o-vision": "gpt-4o",
         "gpt-4o-mini-vision": "gpt-4o-mini",
-        "gpt-4o-mini": "claude",
         "deepseek-chat": "claude-email",
         "deepseek-r1": "deepseek-reasoner",
+        "gemini-2.0": "gemini",
         "gemini-2.0-flash": "gemini",
         "gemini-2.0-flash-thinking": "gemini-thinking",
 
@@ -208,10 +206,8 @@ async def _generate_image(
             "enhance": str(enhance).lower(),
             "safe": str(safe).lower()
         }
-        params = {k: v for k, v in params.items() if v is not None}
-        query = "&".join(f"{k}={quote_plus(v)}" for k, v in params.items())
-        prefix = f"{model}_{seed}" if seed is not None else model
-        url = f"{cls.image_api_endpoint}prompt/{prefix}_{quote_plus(prompt)}?{query}"
+        query = "&".join(f"{k}={quote_plus(v)}" for k, v in params.items() if v is not None)
+        url = f"{cls.image_api_endpoint}prompt/{quote_plus(prompt)}?{query}"
         yield ImagePreview(url, prompt)
 
         async with ClientSession(headers=DEFAULT_HEADERS, connector=get_connector(proxy=proxy)) as session:
@@ -266,7 +262,8 @@ async def _generate_text(
                 "seed": seed,
                 "cache": cache
             })
-
+            if "gemini" in model:
+                data.pop("seed")
             async with session.post(cls.text_api_endpoint, json=data) as response:
                 await raise_for_status(response)
                 result = await response.json()

g4f/Provider/hf/HuggingChat.py

@@ -23,7 +23,7 @@
 from ...requests.raise_for_status import raise_for_status
 from ...providers.response import JsonConversation, ImageResponse, Sources, TitleGeneration, Reasoning, RequestLogin
 from ...cookies import get_cookies
-from .models import default_model, fallback_models, image_models, model_aliases
+from .models import default_model, fallback_models, image_models, model_aliases, llama_models
 from ... import debug
 
 class Conversation(JsonConversation):
@@ -97,6 +97,8 @@ async def create_authed(
     ) -> AsyncResult:
         if not has_curl_cffi:
             raise MissingRequirementsError('Install "curl_cffi" package | pip install -U curl_cffi')
+        if model == llama_models["name"]:
+            model = llama_models["text"] if images is None else llama_models["vision"]
         model = cls.get_model(model)
 
         session = Session(**auth_result.get_dict())

g4f/Provider/hf/HuggingFaceAPI.py

@@ -6,7 +6,7 @@
 from ...errors import ModelNotSupportedError
 from ...providers.helper import get_last_user_message
 from ..template.OpenaiTemplate import OpenaiTemplate
-from .models import model_aliases, vision_models, default_vision_model
+from .models import model_aliases, vision_models, default_vision_model, llama_models
 from .HuggingChat import HuggingChat
 from ... import debug
 
@@ -63,6 +63,8 @@ async def create_async_generator(
     ):
         if model in cls.model_aliases:
             model = cls.model_aliases[model]
+        if model == llama_models["name"]:
+            model = llama_models["text"] if images is None else llama_models["vision"]
         api_base = f"https://api-inference.huggingface.co/models/{model}/v1"
         pipeline_tag = await cls.get_pipline_tag(model, api_key)
         if pipeline_tag not in ("text-generation", "image-text-to-text"):

g4f/Provider/hf/__init__.py

@@ -36,7 +36,7 @@ async def create_async_generator(
         messages: Messages,
         **kwargs
     ) -> AsyncResult:
-        if "images" not in kwargs and "deepseek" in model or random.random() >= 0.5:
+        if "tools" not in kwargs and "images" not in kwargs and "deepseek" in model or random.random() >= 0.5:
             try:
                 is_started = False
                 async for chunk in HuggingFaceInference.create_async_generator(model, messages, **kwargs):

g4f/Provider/hf/models.py

@@ -46,4 +46,9 @@
     "NousResearch/Hermes-3-Llama-3.1-8B",
 ]
 default_vision_model = "meta-llama/Llama-3.2-11B-Vision-Instruct"
-vision_models = [default_vision_model, "Qwen/Qwen2-VL-7B-Instruct"]
+vision_models = [default_vision_model, "Qwen/Qwen2-VL-7B-Instruct"]
+llama_models = {
+    "name": "llama-3",
+    "text": "meta-llama/Llama-3.3-70B-Instruct",
+    "vision": "meta-llama/Llama-3.2-11B-Vision-Instruct",
+}